In [1]:
import pandas as pd
import numpy as np
In [2]:
loan_data = pd.read_csv(r"C:\Users\computer\Desktop\podaci za banke.csv")
C:\Users\computer\AppData\Local\Temp\ipykernel_3596\2462996081.py:1: DtypeWarning: Columns (0,19,59,118,129,130,131,134,135,136,139,145,146,147) have mixed types. Specify dtype option on import or set low_memory=False. loan_data = pd.read_csv(r"C:\Users\computer\Desktop\podaci za banke.csv")
In [3]:
loan_data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1048575 entries, 0 to 1048574 Columns: 151 entries, id to settlement_term dtypes: float64(113), object(38) memory usage: 1.2+ GB
In [4]:
loan_data
Out[4]:
| id | member_id | loan_amnt | funded_amnt | funded_amnt_inv | term | int_rate | installment | grade | sub_grade | ... | hardship_payoff_balance_amount | hardship_last_payment_amount | disbursement_method | debt_settlement_flag | debt_settlement_flag_date | settlement_status | settlement_date | settlement_amount | settlement_percentage | settlement_term | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 68407277 | NaN | 3600.0 | 3600.0 | 3600.0 | 36 months | 13.99 | 123.03 | C | C4 | ... | NaN | NaN | Cash | N | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | 68355089 | NaN | 24700.0 | 24700.0 | 24700.0 | 36 months | 11.99 | 820.28 | C | C1 | ... | NaN | NaN | Cash | N | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | 68341763 | NaN | 20000.0 | 20000.0 | 20000.0 | 60 months | 10.78 | 432.66 | B | B4 | ... | NaN | NaN | Cash | N | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | 66310712 | NaN | 35000.0 | 35000.0 | 35000.0 | 60 months | 14.85 | 829.90 | C | C5 | ... | NaN | NaN | Cash | N | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 68476807 | NaN | 10400.0 | 10400.0 | 10400.0 | 60 months | 22.45 | 289.91 | F | F1 | ... | NaN | NaN | Cash | N | NaN | NaN | NaN | NaN | NaN | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1048570 | 72663394 | NaN | 14400.0 | 14400.0 | 14400.0 | 36 months | 16.29 | 508.33 | D | D1 | ... | NaN | NaN | Cash | Y | May-18 | COMPLETE | Apr-17 | 5510.0 | 42.58 | 12.0 |
| 1048571 | 72673678 | NaN | 10000.0 | 10000.0 | 10000.0 | 36 months | 11.47 | 329.62 | B | B5 | ... | NaN | NaN | Cash | N | NaN | NaN | NaN | NaN | NaN | NaN |
| 1048572 | 72663353 | NaN | 16000.0 | 16000.0 | 16000.0 | 36 months | 10.75 | 521.93 | B | B4 | ... | NaN | NaN | Cash | N | NaN | NaN | NaN | NaN | NaN | NaN |
| 1048573 | 72785737 | NaN | 6000.0 | 6000.0 | 6000.0 | 36 months | 7.89 | 187.72 | A | A5 | ... | NaN | NaN | Cash | N | NaN | NaN | NaN | NaN | NaN | NaN |
| 1048574 | 72573375 | NaN | 1000.0 | 1000.0 | 1000.0 | 36 months | 6.49 | 30.65 | A | A2 | ... | NaN | NaN | Cash | N | NaN | NaN | NaN | NaN | NaN | NaN |
1048575 rows × 151 columns
In [5]:
pd.options.display.max_columns = None
In [6]:
loan_data
Out[6]:
| id | member_id | loan_amnt | funded_amnt | funded_amnt_inv | term | int_rate | installment | grade | sub_grade | emp_title | emp_length | home_ownership | annual_inc | verification_status | issue_d | loan_status | pymnt_plan | url | desc | purpose | title | zip_code | addr_state | dti | delinq_2yrs | earliest_cr_line | fico_range_low | fico_range_high | inq_last_6mths | mths_since_last_delinq | mths_since_last_record | open_acc | pub_rec | revol_bal | revol_util | total_acc | initial_list_status | out_prncp | out_prncp_inv | total_pymnt | total_pymnt_inv | total_rec_prncp | total_rec_int | total_rec_late_fee | recoveries | collection_recovery_fee | last_pymnt_d | last_pymnt_amnt | next_pymnt_d | last_credit_pull_d | last_fico_range_high | last_fico_range_low | collections_12_mths_ex_med | mths_since_last_major_derog | policy_code | application_type | annual_inc_joint | dti_joint | verification_status_joint | acc_now_delinq | tot_coll_amt | tot_cur_bal | open_acc_6m | open_act_il | open_il_12m | open_il_24m | mths_since_rcnt_il | total_bal_il | il_util | open_rv_12m | open_rv_24m | max_bal_bc | all_util | total_rev_hi_lim | inq_fi | total_cu_tl | inq_last_12m | acc_open_past_24mths | avg_cur_bal | bc_open_to_buy | bc_util | chargeoff_within_12_mths | delinq_amnt | mo_sin_old_il_acct | mo_sin_old_rev_tl_op | mo_sin_rcnt_rev_tl_op | mo_sin_rcnt_tl | mort_acc | mths_since_recent_bc | mths_since_recent_bc_dlq | mths_since_recent_inq | mths_since_recent_revol_delinq | num_accts_ever_120_pd | num_actv_bc_tl | num_actv_rev_tl | num_bc_sats | num_bc_tl | num_il_tl | num_op_rev_tl | num_rev_accts | num_rev_tl_bal_gt_0 | num_sats | num_tl_120dpd_2m | num_tl_30dpd | num_tl_90g_dpd_24m | num_tl_op_past_12m | pct_tl_nvr_dlq | percent_bc_gt_75 | pub_rec_bankruptcies | tax_liens | tot_hi_cred_lim | total_bal_ex_mort | total_bc_limit | total_il_high_credit_limit | revol_bal_joint | sec_app_fico_range_low | sec_app_fico_range_high | sec_app_earliest_cr_line | sec_app_inq_last_6mths | sec_app_mort_acc | sec_app_open_acc | sec_app_revol_util | sec_app_open_act_il | sec_app_num_rev_accts | sec_app_chargeoff_within_12_mths | sec_app_collections_12_mths_ex_med | sec_app_mths_since_last_major_derog | hardship_flag | hardship_type | hardship_reason | hardship_status | deferral_term | hardship_amount | hardship_start_date | hardship_end_date | payment_plan_start_date | hardship_length | hardship_dpd | hardship_loan_status | orig_projected_additional_accrued_interest | hardship_payoff_balance_amount | hardship_last_payment_amount | disbursement_method | debt_settlement_flag | debt_settlement_flag_date | settlement_status | settlement_date | settlement_amount | settlement_percentage | settlement_term | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 68407277 | NaN | 3600.0 | 3600.0 | 3600.0 | 36 months | 13.99 | 123.03 | C | C4 | leadman | 10+ years | MORTGAGE | 55000.0 | Not Verified | Dec-15 | Fully Paid | n | https://lendingclub.com/browse/loanDetail.acti... | NaN | debt_consolidation | Debt consolidation | 190xx | PA | 5.91 | 0.0 | Aug-03 | 675.0 | 679.0 | 1.0 | 30.0 | NaN | 7.0 | 0.0 | 2765.0 | 29.7 | 13.0 | w | 0.00 | 0.00 | 4421.723917 | 4421.72 | 3600.00 | 821.72 | 0.0 | 0.0 | 0.0 | Jan-19 | 122.67 | NaN | Mar-19 | 564.0 | 560.0 | 0.0 | 30.0 | 1.0 | Individual | NaN | NaN | NaN | 0.0 | 722.0 | 144904.0 | 2.0 | 2.0 | 0.0 | 1.0 | 21.0 | 4981.0 | 36.0 | 3.0 | 3.0 | 722.0 | 34.0 | 9300.0 | 3.0 | 1.0 | 4.0 | 4.0 | 20701.0 | 1506.0 | 37.2 | 0.0 | 0.0 | 148.0 | 128.0 | 3.0 | 3.0 | 1.0 | 4.0 | 69.0 | 4.0 | 69.0 | 2.0 | 2.0 | 4.0 | 2.0 | 5.0 | 3.0 | 4.0 | 9.0 | 4.0 | 7.0 | 0.0 | 0.0 | 0.0 | 3.0 | 76.9 | 0.0 | 0.0 | 0.0 | 178050.0 | 7746.0 | 2400.0 | 13734.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | N | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Cash | N | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | 68355089 | NaN | 24700.0 | 24700.0 | 24700.0 | 36 months | 11.99 | 820.28 | C | C1 | Engineer | 10+ years | MORTGAGE | 65000.0 | Not Verified | Dec-15 | Fully Paid | n | https://lendingclub.com/browse/loanDetail.acti... | NaN | small_business | Business | 577xx | SD | 16.06 | 1.0 | Dec-99 | 715.0 | 719.0 | 4.0 | 6.0 | NaN | 22.0 | 0.0 | 21470.0 | 19.2 | 38.0 | w | 0.00 | 0.00 | 25679.660000 | 25679.66 | 24700.00 | 979.66 | 0.0 | 0.0 | 0.0 | Jun-16 | 926.35 | NaN | Mar-19 | 699.0 | 695.0 | 0.0 | NaN | 1.0 | Individual | NaN | NaN | NaN | 0.0 | 0.0 | 204396.0 | 1.0 | 1.0 | 0.0 | 1.0 | 19.0 | 18005.0 | 73.0 | 2.0 | 3.0 | 6472.0 | 29.0 | 111800.0 | 0.0 | 0.0 | 6.0 | 4.0 | 9733.0 | 57830.0 | 27.1 | 0.0 | 0.0 | 113.0 | 192.0 | 2.0 | 2.0 | 4.0 | 2.0 | NaN | 0.0 | 6.0 | 0.0 | 5.0 | 5.0 | 13.0 | 17.0 | 6.0 | 20.0 | 27.0 | 5.0 | 22.0 | 0.0 | 0.0 | 0.0 | 2.0 | 97.4 | 7.7 | 0.0 | 0.0 | 314017.0 | 39475.0 | 79300.0 | 24667.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | N | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Cash | N | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | 68341763 | NaN | 20000.0 | 20000.0 | 20000.0 | 60 months | 10.78 | 432.66 | B | B4 | truck driver | 10+ years | MORTGAGE | 63000.0 | Not Verified | Dec-15 | Fully Paid | n | https://lendingclub.com/browse/loanDetail.acti... | NaN | home_improvement | NaN | 605xx | IL | 10.78 | 0.0 | Aug-00 | 695.0 | 699.0 | 0.0 | NaN | NaN | 6.0 | 0.0 | 7869.0 | 56.2 | 18.0 | w | 0.00 | 0.00 | 22705.924290 | 22705.92 | 20000.00 | 2705.92 | 0.0 | 0.0 | 0.0 | Jun-17 | 15813.30 | NaN | Mar-19 | 704.0 | 700.0 | 0.0 | NaN | 1.0 | Joint App | 71000.0 | 13.85 | Not Verified | 0.0 | 0.0 | 189699.0 | 0.0 | 1.0 | 0.0 | 4.0 | 19.0 | 10827.0 | 73.0 | 0.0 | 2.0 | 2081.0 | 65.0 | 14000.0 | 2.0 | 5.0 | 1.0 | 6.0 | 31617.0 | 2737.0 | 55.9 | 0.0 | 0.0 | 125.0 | 184.0 | 14.0 | 14.0 | 5.0 | 101.0 | NaN | 10.0 | NaN | 0.0 | 2.0 | 3.0 | 2.0 | 4.0 | 6.0 | 4.0 | 7.0 | 3.0 | 6.0 | 0.0 | 0.0 | 0.0 | 0.0 | 100.0 | 50.0 | 0.0 | 0.0 | 218418.0 | 18696.0 | 6200.0 | 14877.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | N | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Cash | N | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | 66310712 | NaN | 35000.0 | 35000.0 | 35000.0 | 60 months | 14.85 | 829.90 | C | C5 | Information Systems Officer | 10+ years | MORTGAGE | 110000.0 | Source Verified | Dec-15 | Current | n | https://lendingclub.com/browse/loanDetail.acti... | NaN | debt_consolidation | Debt consolidation | 076xx | NJ | 17.06 | 0.0 | Sep-08 | 785.0 | 789.0 | 0.0 | NaN | NaN | 13.0 | 0.0 | 7802.0 | 11.6 | 17.0 | w | 15897.65 | 15897.65 | 31464.010000 | 31464.01 | 19102.35 | 12361.66 | 0.0 | 0.0 | 0.0 | Feb-19 | 829.90 | Apr-19 | Mar-19 | 679.0 | 675.0 | 0.0 | NaN | 1.0 | Individual | NaN | NaN | NaN | 0.0 | 0.0 | 301500.0 | 1.0 | 1.0 | 0.0 | 1.0 | 23.0 | 12609.0 | 70.0 | 1.0 | 1.0 | 6987.0 | 45.0 | 67300.0 | 0.0 | 1.0 | 0.0 | 2.0 | 23192.0 | 54962.0 | 12.1 | 0.0 | 0.0 | 36.0 | 87.0 | 2.0 | 2.0 | 1.0 | 2.0 | NaN | NaN | NaN | 0.0 | 4.0 | 5.0 | 8.0 | 10.0 | 2.0 | 10.0 | 13.0 | 5.0 | 13.0 | 0.0 | 0.0 | 0.0 | 1.0 | 100.0 | 0.0 | 0.0 | 0.0 | 381215.0 | 52226.0 | 62500.0 | 18000.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | N | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Cash | N | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 68476807 | NaN | 10400.0 | 10400.0 | 10400.0 | 60 months | 22.45 | 289.91 | F | F1 | Contract Specialist | 3 years | MORTGAGE | 104433.0 | Source Verified | Dec-15 | Fully Paid | n | https://lendingclub.com/browse/loanDetail.acti... | NaN | major_purchase | Major purchase | 174xx | PA | 25.37 | 1.0 | Jun-98 | 695.0 | 699.0 | 3.0 | 12.0 | NaN | 12.0 | 0.0 | 21929.0 | 64.5 | 35.0 | w | 0.00 | 0.00 | 11740.500000 | 11740.50 | 10400.00 | 1340.50 | 0.0 | 0.0 | 0.0 | Jul-16 | 10128.96 | NaN | Mar-18 | 704.0 | 700.0 | 0.0 | NaN | 1.0 | Individual | NaN | NaN | NaN | 0.0 | 0.0 | 331730.0 | 1.0 | 3.0 | 0.0 | 3.0 | 14.0 | 73839.0 | 84.0 | 4.0 | 7.0 | 9702.0 | 78.0 | 34000.0 | 2.0 | 1.0 | 3.0 | 10.0 | 27644.0 | 4567.0 | 77.5 | 0.0 | 0.0 | 128.0 | 210.0 | 4.0 | 4.0 | 6.0 | 4.0 | 12.0 | 1.0 | 12.0 | 0.0 | 4.0 | 6.0 | 5.0 | 9.0 | 10.0 | 7.0 | 19.0 | 6.0 | 12.0 | 0.0 | 0.0 | 0.0 | 4.0 | 96.6 | 60.0 | 0.0 | 0.0 | 439570.0 | 95768.0 | 20300.0 | 88097.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | N | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Cash | N | NaN | NaN | NaN | NaN | NaN | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1048570 | 72663394 | NaN | 14400.0 | 14400.0 | 14400.0 | 36 months | 16.29 | 508.33 | D | D1 | Purchasing manager | 10+ years | RENT | 45000.0 | Not Verified | Feb-16 | Charged Off | n | https://lendingclub.com/browse/loanDetail.acti... | NaN | credit_card | Credit card refinancing | 168xx | PA | 33.76 | 0.0 | Nov-03 | 675.0 | 679.0 | 0.0 | NaN | NaN | 9.0 | 0.0 | 20018.0 | 65.8 | 15.0 | w | 0.00 | 0.00 | 8996.630000 | 8996.63 | 2281.17 | 1205.46 | 0.0 | 5510.0 | 991.8 | Sep-16 | 508.33 | NaN | Aug-18 | 629.0 | 625.0 | 0.0 | NaN | 1.0 | Individual | NaN | NaN | NaN | 0.0 | 214.0 | 42230.0 | 0.0 | 2.0 | 0.0 | 1.0 | 24.0 | 22212.0 | 61.0 | 1.0 | 2.0 | 10936.0 | 63.0 | 30400.0 | 0.0 | 1.0 | 0.0 | 3.0 | 4692.0 | 8516.0 | 70.1 | 0.0 | 0.0 | 131.0 | 147.0 | 8.0 | 8.0 | 1.0 | 37.0 | NaN | 23.0 | NaN | 0.0 | 4.0 | 5.0 | 5.0 | 5.0 | 5.0 | 7.0 | 9.0 | 5.0 | 9.0 | 0.0 | 0.0 | 0.0 | 1.0 | 100.0 | 80.0 | 0.0 | 0.0 | 66617.0 | 42230.0 | 28500.0 | 36217.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | N | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Cash | Y | May-18 | COMPLETE | Apr-17 | 5510.0 | 42.58 | 12.0 |
| 1048571 | 72673678 | NaN | 10000.0 | 10000.0 | 10000.0 | 36 months | 11.47 | 329.62 | B | B5 | E6 | 10+ years | MORTGAGE | 135000.0 | Not Verified | Feb-16 | Fully Paid | n | https://lendingclub.com/browse/loanDetail.acti... | NaN | debt_consolidation | Debt consolidation | 985xx | WA | 26.41 | 1.0 | Oct-03 | 665.0 | 669.0 | 0.0 | 15.0 | NaN | 22.0 | 0.0 | 23617.0 | 71.1 | 37.0 | w | 0.00 | 0.00 | 11914.718730 | 11914.72 | 10000.00 | 1914.72 | 0.0 | 0.0 | 0.0 | Jan-19 | 1002.22 | NaN | Jan-19 | 669.0 | 665.0 | 0.0 | NaN | 1.0 | Individual | NaN | NaN | NaN | 0.0 | 0.0 | 269082.0 | 3.0 | 3.0 | 3.0 | 3.0 | 9.0 | 52237.0 | 75.0 | 2.0 | 3.0 | 2873.0 | 74.0 | 33200.0 | 0.0 | 4.0 | 3.0 | 8.0 | 12231.0 | 4849.0 | 68.7 | 0.0 | 0.0 | 119.0 | 148.0 | 2.0 | 1.0 | 3.0 | 2.0 | 15.0 | 5.0 | 15.0 | 0.0 | 7.0 | 11.0 | 10.0 | 13.0 | 13.0 | 17.0 | 20.0 | 11.0 | 22.0 | 0.0 | 0.0 | 0.0 | 7.0 | 97.3 | 50.0 | 0.0 | 0.0 | 297427.0 | 76297.0 | 15500.0 | 70097.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | N | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Cash | N | NaN | NaN | NaN | NaN | NaN | NaN |
| 1048572 | 72663353 | NaN | 16000.0 | 16000.0 | 16000.0 | 36 months | 10.75 | 521.93 | B | B4 | Consultant | < 1 year | MORTGAGE | 70000.0 | Source Verified | Feb-16 | Fully Paid | n | https://lendingclub.com/browse/loanDetail.acti... | NaN | debt_consolidation | Debt consolidation | 284xx | NC | 21.67 | 0.0 | May-90 | 710.0 | 714.0 | 0.0 | NaN | NaN | 16.0 | 0.0 | 45926.0 | 55.2 | 30.0 | w | 0.00 | 0.00 | 18727.515200 | 18727.52 | 16000.00 | 2727.52 | 0.0 | 0.0 | 0.0 | Nov-18 | 2303.27 | NaN | Mar-19 | 704.0 | 700.0 | 0.0 | NaN | 1.0 | Individual | NaN | NaN | NaN | 0.0 | 0.0 | 253889.0 | 0.0 | 1.0 | 0.0 | 0.0 | 27.0 | 8633.0 | 39.0 | 1.0 | 2.0 | 14809.0 | 52.0 | 83200.0 | 0.0 | 1.0 | 0.0 | 2.0 | 15868.0 | 4494.0 | 90.1 | 0.0 | 0.0 | 48.0 | 309.0 | 8.0 | 8.0 | 3.0 | 17.0 | NaN | 17.0 | NaN | 0.0 | 5.0 | 7.0 | 7.0 | 12.0 | 3.0 | 14.0 | 24.0 | 7.0 | 15.0 | 0.0 | 0.0 | 0.0 | 1.0 | 100.0 | 71.4 | 0.0 | 0.0 | 326583.0 | 54559.0 | 45200.0 | 22383.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | N | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Cash | N | NaN | NaN | NaN | NaN | NaN | NaN |
| 1048573 | 72785737 | NaN | 6000.0 | 6000.0 | 6000.0 | 36 months | 7.89 | 187.72 | A | A5 | Administrative Assistant | 6 years | MORTGAGE | 31500.0 | Source Verified | Feb-16 | Fully Paid | n | https://lendingclub.com/browse/loanDetail.acti... | NaN | debt_consolidation | NaN | 394xx | MS | 25.98 | 0.0 | Mar-89 | 715.0 | 719.0 | 0.0 | 49.0 | NaN | 11.0 | 0.0 | 7564.0 | 27.6 | 35.0 | w | 0.00 | 0.00 | 6752.411954 | 6752.41 | 6000.00 | 752.41 | 0.0 | 0.0 | 0.0 | Feb-19 | 187.47 | NaN | Mar-19 | 774.0 | 770.0 | 0.0 | NaN | 1.0 | Individual | NaN | NaN | NaN | 0.0 | 220.0 | 48672.0 | 1.0 | 2.0 | 1.0 | 3.0 | 8.0 | 10031.0 | 78.0 | 2.0 | 4.0 | 3129.0 | 44.0 | 27400.0 | 0.0 | 0.0 | 1.0 | 7.0 | 4867.0 | 8671.0 | 37.2 | 0.0 | 0.0 | 147.0 | 323.0 | 4.0 | 4.0 | 2.0 | 4.0 | NaN | 8.0 | NaN | 0.0 | 4.0 | 6.0 | 4.0 | 5.0 | 17.0 | 8.0 | 16.0 | 6.0 | 11.0 | 0.0 | 0.0 | 0.0 | 3.0 | 97.1 | 25.0 | 0.0 | 0.0 | 92330.0 | 17595.0 | 13800.0 | 12930.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | N | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Cash | N | NaN | NaN | NaN | NaN | NaN | NaN |
| 1048574 | 72573375 | NaN | 1000.0 | 1000.0 | 1000.0 | 36 months | 6.49 | 30.65 | A | A2 | TEACHER | 10+ years | MORTGAGE | 65000.0 | Not Verified | Feb-16 | Fully Paid | n | https://lendingclub.com/browse/loanDetail.acti... | NaN | home_improvement | Home improvement | 147xx | NY | 8.33 | 0.0 | Oct-98 | 755.0 | 759.0 | 0.0 | 81.0 | 94.0 | 7.0 | 1.0 | 648.0 | 4.3 | 17.0 | w | 0.00 | 0.00 | 1026.210000 | 1026.21 | 1000.00 | 26.21 | 0.0 | 0.0 | 0.0 | Jul-16 | 904.33 | NaN | Mar-19 | 769.0 | 765.0 | 0.0 | NaN | 1.0 | Individual | NaN | NaN | NaN | 0.0 | 0.0 | 202423.0 | 0.0 | 3.0 | 0.0 | 2.0 | 16.0 | 50388.0 | 34.0 | 1.0 | 2.0 | 648.0 | 19.0 | 15000.0 | 0.0 | 0.0 | 1.0 | 4.0 | 28918.0 | 14352.0 | 4.3 | 0.0 | 0.0 | 208.0 | 89.0 | 7.0 | 7.0 | 6.0 | 18.0 | NaN | 7.0 | NaN | 0.0 | 1.0 | 1.0 | 2.0 | 2.0 | 8.0 | 2.0 | 3.0 | 1.0 | 7.0 | 0.0 | 0.0 | 0.0 | 1.0 | 88.2 | 0.0 | 1.0 | 0.0 | 255323.0 | 51036.0 | 15000.0 | 60323.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | N | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Cash | N | NaN | NaN | NaN | NaN | NaN | NaN |
1048575 rows × 151 columns
In [7]:
# Print a detailed view of column names and types
for col, dtype in loan_data.dtypes.items():
print(f"Column: {col}, Data Type: {dtype}")
Column: id, Data Type: object Column: member_id, Data Type: float64 Column: loan_amnt, Data Type: float64 Column: funded_amnt, Data Type: float64 Column: funded_amnt_inv, Data Type: float64 Column: term, Data Type: object Column: int_rate, Data Type: float64 Column: installment, Data Type: float64 Column: grade, Data Type: object Column: sub_grade, Data Type: object Column: emp_title, Data Type: object Column: emp_length, Data Type: object Column: home_ownership, Data Type: object Column: annual_inc, Data Type: float64 Column: verification_status, Data Type: object Column: issue_d, Data Type: object Column: loan_status, Data Type: object Column: pymnt_plan, Data Type: object Column: url, Data Type: object Column: desc, Data Type: object Column: purpose, Data Type: object Column: title, Data Type: object Column: zip_code, Data Type: object Column: addr_state, Data Type: object Column: dti, Data Type: float64 Column: delinq_2yrs, Data Type: float64 Column: earliest_cr_line, Data Type: object Column: fico_range_low, Data Type: float64 Column: fico_range_high, Data Type: float64 Column: inq_last_6mths, Data Type: float64 Column: mths_since_last_delinq, Data Type: float64 Column: mths_since_last_record, Data Type: float64 Column: open_acc, Data Type: float64 Column: pub_rec, Data Type: float64 Column: revol_bal, Data Type: float64 Column: revol_util, Data Type: float64 Column: total_acc, Data Type: float64 Column: initial_list_status, Data Type: object Column: out_prncp, Data Type: float64 Column: out_prncp_inv, Data Type: float64 Column: total_pymnt, Data Type: float64 Column: total_pymnt_inv, Data Type: float64 Column: total_rec_prncp, Data Type: float64 Column: total_rec_int, Data Type: float64 Column: total_rec_late_fee, Data Type: float64 Column: recoveries, Data Type: float64 Column: collection_recovery_fee, Data Type: float64 Column: last_pymnt_d, Data Type: object Column: last_pymnt_amnt, Data Type: float64 Column: next_pymnt_d, Data Type: object Column: last_credit_pull_d, Data Type: object Column: last_fico_range_high, Data Type: float64 Column: last_fico_range_low, Data Type: float64 Column: collections_12_mths_ex_med, Data Type: float64 Column: mths_since_last_major_derog, Data Type: float64 Column: policy_code, Data Type: float64 Column: application_type, Data Type: object Column: annual_inc_joint, Data Type: float64 Column: dti_joint, Data Type: float64 Column: verification_status_joint, Data Type: object Column: acc_now_delinq, Data Type: float64 Column: tot_coll_amt, Data Type: float64 Column: tot_cur_bal, Data Type: float64 Column: open_acc_6m, Data Type: float64 Column: open_act_il, Data Type: float64 Column: open_il_12m, Data Type: float64 Column: open_il_24m, Data Type: float64 Column: mths_since_rcnt_il, Data Type: float64 Column: total_bal_il, Data Type: float64 Column: il_util, Data Type: float64 Column: open_rv_12m, Data Type: float64 Column: open_rv_24m, Data Type: float64 Column: max_bal_bc, Data Type: float64 Column: all_util, Data Type: float64 Column: total_rev_hi_lim, Data Type: float64 Column: inq_fi, Data Type: float64 Column: total_cu_tl, Data Type: float64 Column: inq_last_12m, Data Type: float64 Column: acc_open_past_24mths, Data Type: float64 Column: avg_cur_bal, Data Type: float64 Column: bc_open_to_buy, Data Type: float64 Column: bc_util, Data Type: float64 Column: chargeoff_within_12_mths, Data Type: float64 Column: delinq_amnt, Data Type: float64 Column: mo_sin_old_il_acct, Data Type: float64 Column: mo_sin_old_rev_tl_op, Data Type: float64 Column: mo_sin_rcnt_rev_tl_op, Data Type: float64 Column: mo_sin_rcnt_tl, Data Type: float64 Column: mort_acc, Data Type: float64 Column: mths_since_recent_bc, Data Type: float64 Column: mths_since_recent_bc_dlq, Data Type: float64 Column: mths_since_recent_inq, Data Type: float64 Column: mths_since_recent_revol_delinq, Data Type: float64 Column: num_accts_ever_120_pd, Data Type: float64 Column: num_actv_bc_tl, Data Type: float64 Column: num_actv_rev_tl, Data Type: float64 Column: num_bc_sats, Data Type: float64 Column: num_bc_tl, Data Type: float64 Column: num_il_tl, Data Type: float64 Column: num_op_rev_tl, Data Type: float64 Column: num_rev_accts, Data Type: float64 Column: num_rev_tl_bal_gt_0, Data Type: float64 Column: num_sats, Data Type: float64 Column: num_tl_120dpd_2m, Data Type: float64 Column: num_tl_30dpd, Data Type: float64 Column: num_tl_90g_dpd_24m, Data Type: float64 Column: num_tl_op_past_12m, Data Type: float64 Column: pct_tl_nvr_dlq, Data Type: float64 Column: percent_bc_gt_75, Data Type: float64 Column: pub_rec_bankruptcies, Data Type: float64 Column: tax_liens, Data Type: float64 Column: tot_hi_cred_lim, Data Type: float64 Column: total_bal_ex_mort, Data Type: float64 Column: total_bc_limit, Data Type: float64 Column: total_il_high_credit_limit, Data Type: float64 Column: revol_bal_joint, Data Type: float64 Column: sec_app_fico_range_low, Data Type: float64 Column: sec_app_fico_range_high, Data Type: float64 Column: sec_app_earliest_cr_line, Data Type: object Column: sec_app_inq_last_6mths, Data Type: float64 Column: sec_app_mort_acc, Data Type: float64 Column: sec_app_open_acc, Data Type: float64 Column: sec_app_revol_util, Data Type: float64 Column: sec_app_open_act_il, Data Type: float64 Column: sec_app_num_rev_accts, Data Type: float64 Column: sec_app_chargeoff_within_12_mths, Data Type: float64 Column: sec_app_collections_12_mths_ex_med, Data Type: float64 Column: sec_app_mths_since_last_major_derog, Data Type: float64 Column: hardship_flag, Data Type: object Column: hardship_type, Data Type: object Column: hardship_reason, Data Type: object Column: hardship_status, Data Type: object Column: deferral_term, Data Type: float64 Column: hardship_amount, Data Type: float64 Column: hardship_start_date, Data Type: object Column: hardship_end_date, Data Type: object Column: payment_plan_start_date, Data Type: object Column: hardship_length, Data Type: float64 Column: hardship_dpd, Data Type: float64 Column: hardship_loan_status, Data Type: object Column: orig_projected_additional_accrued_interest, Data Type: float64 Column: hardship_payoff_balance_amount, Data Type: float64 Column: hardship_last_payment_amount, Data Type: float64 Column: disbursement_method, Data Type: object Column: debt_settlement_flag, Data Type: object Column: debt_settlement_flag_date, Data Type: object Column: settlement_status, Data Type: object Column: settlement_date, Data Type: object Column: settlement_amount, Data Type: float64 Column: settlement_percentage, Data Type: float64 Column: settlement_term, Data Type: float64
In [8]:
loan_data['emp_length'].unique()
Out[8]:
array(['10+ years', '3 years', '4 years', '6 years', '1 year', '7 years',
'8 years', '5 years', '2 years', '9 years', '< 1 year', nan],
dtype=object)
In [9]:
# Clean the 'emp_length' column directly in loan_data
loan_data['emp_length_int'] = (
loan_data['emp_length']
.str.strip() # Remove leading/trailing spaces
.replace(
{
r'\+ years': '', # Remove '+ years'
r'<1 year': '0', # Replace '<1 year' with '0'
r' years': '', # Remove ' years'
r' year': '', # Remove ' year'
r'n/a': '0' # Replace 'n/a' with '0'
},
regex=True
)
)
# Convert to numeric, coercing any errors
loan_data['emp_length_int'] = pd.to_numeric(loan_data['emp_length_int'], errors='coerce')
# Replace NaN values with 0
loan_data['emp_length_int'] = loan_data['emp_length_int'].fillna(0)
# Display the updated DataFrame
print(loan_data[[ 'emp_length_int']]) # Show original and cleaned columns
emp_length_int 0 10.0 1 10.0 2 10.0 3 10.0 4 3.0 ... ... 1048570 10.0 1048571 10.0 1048572 0.0 1048573 6.0 1048574 10.0 [1048575 rows x 1 columns]
In [10]:
loan_data['term'].unique()
Out[10]:
array([' 36 months', ' 60 months', nan], dtype=object)
In [11]:
# Clean the 'term' column directly in loan_data
loan_data['term_int'] = (
loan_data['term']
.str.replace(' months', '', regex=True) # Remove ' months'
)
# Convert to numeric, coercing any errors
loan_data['term_int'] = pd.to_numeric(loan_data['term_int'], errors='coerce')
# Display the updated DataFrame
print(loan_data[['term_int']]) # Show both original and cleaned columns
term_int 0 36.0 1 36.0 2 60.0 3 60.0 4 60.0 ... ... 1048570 36.0 1048571 36.0 1048572 36.0 1048573 36.0 1048574 36.0 [1048575 rows x 1 columns]
In [12]:
loan_data['earliest_cr_line']
Out[12]:
0 Aug-03
1 Dec-99
2 Aug-00
3 Sep-08
4 Jun-98
...
1048570 Nov-03
1048571 Oct-03
1048572 May-90
1048573 Mar-89
1048574 Oct-98
Name: earliest_cr_line, Length: 1048575, dtype: object
In [13]:
import pandas as pd
import numpy as np
# Assuming 'loan_data' is already a DataFrame and the 'earliest_cr_line' column exists
# Convert to datetime
loan_data['earliest_cr_line_date'] = pd.to_datetime(loan_data['earliest_cr_line'], format='%b-%y', errors='coerce')
# Calculate the difference in months using pd.DateOffset
reference_date = pd.to_datetime('2024-12-01')
loan_data['mth_earliest_cr_line_date'] = loan_data['earliest_cr_line_date'].apply(
lambda x: (reference_date.year - x.year) * 12 + reference_date.month - x.month
)
In [38]:
import pandas as pd
import numpy as np
# Convert to datetime
loan_data['earliest_cr_line_date'] = pd.to_datetime(loan_data['earliest_cr_line'], format='%b-%y', errors='coerce')
# Calculate the difference in years and months
loan_data['mth_earliest_cr_line_date'] = (
(pd.to_datetime('2024-12-01') - loan_data['earliest_cr_line_date']).dt.days // 30
)
loan_data['mth_earliest_cr_line_date'] = loan_data['mth_earliest_cr_line_date'].round()
# Check the result
print(loan_data['mth_earliest_cr_line_date'])
0 259.0
1 304.0
2 296.0
3 197.0
4 322.0
...
1048570 256.0
1048571 257.0
1048572 421.0
1048573 435.0
1048574 318.0
Name: mth_earliest_cr_line_date, Length: 1048575, dtype: float64
In [39]:
loan_data['mth_earliest_cr_line_date'].describe()
Out[39]:
count 1.048563e+06 mean 2.987901e+02 std 1.008662e+02 min -5.360000e+02 25% 2.360000e+02 50% 2.840000e+02 75% 3.510000e+02 max 6.800000e+02 Name: mth_earliest_cr_line_date, dtype: float64
In [40]:
loan_data.loc [: , ['earliest_cr_line', 'earliest_cr_line_date','mth_earliest_cr_line_date' ]][loan_data['mth_earliest_cr_line_date'] <0]
Out[40]:
| earliest_cr_line | earliest_cr_line_date | mth_earliest_cr_line_date | |
|---|---|---|---|
| 135 | Jan-57 | 2057-01-01 | -391.0 |
| 480 | Jul-68 | 2068-07-01 | -531.0 |
| 551 | Aug-64 | 2064-08-01 | -483.0 |
| 1654 | Jan-65 | 2065-01-01 | -489.0 |
| 2757 | Sep-64 | 2064-09-01 | -484.0 |
| ... | ... | ... | ... |
| 1047542 | Jan-62 | 2062-01-01 | -452.0 |
| 1047998 | Dec-66 | 2066-12-01 | -512.0 |
| 1048113 | Apr-68 | 2068-04-01 | -528.0 |
| 1048137 | Jan-62 | 2062-01-01 | -452.0 |
| 1048556 | Nov-68 | 2068-11-01 | -535.0 |
2364 rows × 3 columns
In [42]:
loan_data['mth_earliest_cr_line_date'][loan_data['mth_earliest_cr_line_date'] < 0] = loan_data['mth_earliest_cr_line_date'].max()
C:\Users\computer\AppData\Local\Temp\ipykernel_3596\1221912367.py:1: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy loan_data['mth_earliest_cr_line_date'][loan_data['mth_earliest_cr_line_date'] < 0] = loan_data['mth_earliest_cr_line_date'].max() C:\Users\computer\AppData\Local\Temp\ipykernel_3596\1221912367.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy loan_data['mth_earliest_cr_line_date'][loan_data['mth_earliest_cr_line_date'] < 0] = loan_data['mth_earliest_cr_line_date'].max()
In [43]:
min(loan_data['mth_earliest_cr_line_date'])
Out[43]:
113.0
In [44]:
pd.get_dummies(loan_data['grade'], prefix = 'grade', prefix_sep = ':')
Out[44]:
| grade:A | grade:B | grade:C | grade:D | grade:E | grade:F | grade:G | |
|---|---|---|---|---|---|---|---|
| 0 | False | False | True | False | False | False | False |
| 1 | False | False | True | False | False | False | False |
| 2 | False | True | False | False | False | False | False |
| 3 | False | False | True | False | False | False | False |
| 4 | False | False | False | False | False | True | False |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 1048570 | False | False | False | True | False | False | False |
| 1048571 | False | True | False | False | False | False | False |
| 1048572 | False | True | False | False | False | False | False |
| 1048573 | True | False | False | False | False | False | False |
| 1048574 | True | False | False | False | False | False | False |
1048575 rows × 7 columns
In [45]:
loan_data_dummies = [pd.get_dummies(loan_data['grade'], prefix = 'grade', prefix_sep = ':'),
pd.get_dummies(loan_data['sub_grade'], prefix = 'sub_grade', prefix_sep = ':'),
pd.get_dummies(loan_data['home_ownership'], prefix = 'home_ownership', prefix_sep = ':'),
pd.get_dummies(loan_data['verification_status'], prefix = 'verification_status', prefix_sep = ':'),
pd.get_dummies(loan_data['loan_status'], prefix = 'loan_status', prefix_sep = ':'),
pd.get_dummies(loan_data['purpose'], prefix = 'purpose', prefix_sep = ':'),
pd.get_dummies(loan_data['addr_state'], prefix = 'addr_state', prefix_sep = ':'),
pd.get_dummies(loan_data['initial_list_status'], prefix = 'initial_list_status', prefix_sep = ':')]
In [46]:
loan_data_dummies = pd.concat(loan_data_dummies, axis = 1)
In [47]:
type(loan_data_dummies)
Out[47]:
pandas.core.frame.DataFrame
In [48]:
loan_data_dummies = pd.concat([loan_data,loan_data_dummies], axis = 1)
In [49]:
loan_data_dummies.columns.values
Out[49]:
array(['id', 'member_id', 'loan_amnt', 'funded_amnt', 'funded_amnt_inv',
'term', 'int_rate', 'installment', 'grade', 'sub_grade',
'emp_title', 'emp_length', 'home_ownership', 'annual_inc',
'verification_status', 'issue_d', 'loan_status', 'pymnt_plan',
'url', 'desc', 'purpose', 'title', 'zip_code', 'addr_state', 'dti',
'delinq_2yrs', 'earliest_cr_line', 'fico_range_low',
'fico_range_high', 'inq_last_6mths', 'mths_since_last_delinq',
'mths_since_last_record', 'open_acc', 'pub_rec', 'revol_bal',
'revol_util', 'total_acc', 'initial_list_status', 'out_prncp',
'out_prncp_inv', 'total_pymnt', 'total_pymnt_inv',
'total_rec_prncp', 'total_rec_int', 'total_rec_late_fee',
'recoveries', 'collection_recovery_fee', 'last_pymnt_d',
'last_pymnt_amnt', 'next_pymnt_d', 'last_credit_pull_d',
'last_fico_range_high', 'last_fico_range_low',
'collections_12_mths_ex_med', 'mths_since_last_major_derog',
'policy_code', 'application_type', 'annual_inc_joint', 'dti_joint',
'verification_status_joint', 'acc_now_delinq', 'tot_coll_amt',
'tot_cur_bal', 'open_acc_6m', 'open_act_il', 'open_il_12m',
'open_il_24m', 'mths_since_rcnt_il', 'total_bal_il', 'il_util',
'open_rv_12m', 'open_rv_24m', 'max_bal_bc', 'all_util',
'total_rev_hi_lim', 'inq_fi', 'total_cu_tl', 'inq_last_12m',
'acc_open_past_24mths', 'avg_cur_bal', 'bc_open_to_buy', 'bc_util',
'chargeoff_within_12_mths', 'delinq_amnt', 'mo_sin_old_il_acct',
'mo_sin_old_rev_tl_op', 'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl',
'mort_acc', 'mths_since_recent_bc', 'mths_since_recent_bc_dlq',
'mths_since_recent_inq', 'mths_since_recent_revol_delinq',
'num_accts_ever_120_pd', 'num_actv_bc_tl', 'num_actv_rev_tl',
'num_bc_sats', 'num_bc_tl', 'num_il_tl', 'num_op_rev_tl',
'num_rev_accts', 'num_rev_tl_bal_gt_0', 'num_sats',
'num_tl_120dpd_2m', 'num_tl_30dpd', 'num_tl_90g_dpd_24m',
'num_tl_op_past_12m', 'pct_tl_nvr_dlq', 'percent_bc_gt_75',
'pub_rec_bankruptcies', 'tax_liens', 'tot_hi_cred_lim',
'total_bal_ex_mort', 'total_bc_limit',
'total_il_high_credit_limit', 'revol_bal_joint',
'sec_app_fico_range_low', 'sec_app_fico_range_high',
'sec_app_earliest_cr_line', 'sec_app_inq_last_6mths',
'sec_app_mort_acc', 'sec_app_open_acc', 'sec_app_revol_util',
'sec_app_open_act_il', 'sec_app_num_rev_accts',
'sec_app_chargeoff_within_12_mths',
'sec_app_collections_12_mths_ex_med',
'sec_app_mths_since_last_major_derog', 'hardship_flag',
'hardship_type', 'hardship_reason', 'hardship_status',
'deferral_term', 'hardship_amount', 'hardship_start_date',
'hardship_end_date', 'payment_plan_start_date', 'hardship_length',
'hardship_dpd', 'hardship_loan_status',
'orig_projected_additional_accrued_interest',
'hardship_payoff_balance_amount', 'hardship_last_payment_amount',
'disbursement_method', 'debt_settlement_flag',
'debt_settlement_flag_date', 'settlement_status',
'settlement_date', 'settlement_amount', 'settlement_percentage',
'settlement_term', 'emp_length_int', 'term_int',
'earliest_cr_line_date', 'mth_earliest_cr_line_date', 'grade:A',
'grade:B', 'grade:C', 'grade:D', 'grade:E', 'grade:F', 'grade:G',
'sub_grade:A1', 'sub_grade:A2', 'sub_grade:A3', 'sub_grade:A4',
'sub_grade:A5', 'sub_grade:B1', 'sub_grade:B2', 'sub_grade:B3',
'sub_grade:B4', 'sub_grade:B5', 'sub_grade:C1', 'sub_grade:C2',
'sub_grade:C3', 'sub_grade:C4', 'sub_grade:C5', 'sub_grade:D1',
'sub_grade:D2', 'sub_grade:D3', 'sub_grade:D4', 'sub_grade:D5',
'sub_grade:E1', 'sub_grade:E2', 'sub_grade:E3', 'sub_grade:E4',
'sub_grade:E5', 'sub_grade:F1', 'sub_grade:F2', 'sub_grade:F3',
'sub_grade:F4', 'sub_grade:F5', 'sub_grade:G1', 'sub_grade:G2',
'sub_grade:G3', 'sub_grade:G4', 'sub_grade:G5',
'home_ownership:ANY', 'home_ownership:MORTGAGE',
'home_ownership:NONE', 'home_ownership:OWN', 'home_ownership:RENT',
'verification_status:Not Verified',
'verification_status:Source Verified',
'verification_status:Verified', 'loan_status:Charged Off',
'loan_status:Current', 'loan_status:Default',
'loan_status:Fully Paid', 'loan_status:In Grace Period',
'loan_status:Late (16-30 days)', 'loan_status:Late (31-120 days)',
'purpose:car', 'purpose:credit_card', 'purpose:debt_consolidation',
'purpose:educational', 'purpose:home_improvement', 'purpose:house',
'purpose:major_purchase', 'purpose:medical', 'purpose:moving',
'purpose:other', 'purpose:renewable_energy',
'purpose:small_business', 'purpose:vacation', 'purpose:wedding',
'addr_state:AK', 'addr_state:AL', 'addr_state:AR', 'addr_state:AZ',
'addr_state:CA', 'addr_state:CO', 'addr_state:CT', 'addr_state:DC',
'addr_state:DE', 'addr_state:FL', 'addr_state:GA', 'addr_state:HI',
'addr_state:ID', 'addr_state:IL', 'addr_state:IN', 'addr_state:KS',
'addr_state:KY', 'addr_state:LA', 'addr_state:MA', 'addr_state:MD',
'addr_state:ME', 'addr_state:MI', 'addr_state:MN', 'addr_state:MO',
'addr_state:MS', 'addr_state:MT', 'addr_state:NC', 'addr_state:ND',
'addr_state:NE', 'addr_state:NH', 'addr_state:NJ', 'addr_state:NM',
'addr_state:NV', 'addr_state:NY', 'addr_state:OH', 'addr_state:OK',
'addr_state:OR', 'addr_state:PA', 'addr_state:RI', 'addr_state:SC',
'addr_state:SD', 'addr_state:TN', 'addr_state:TX', 'addr_state:UT',
'addr_state:VA', 'addr_state:VT', 'addr_state:WA', 'addr_state:WI',
'addr_state:WV', 'addr_state:WY', 'initial_list_status:f',
'initial_list_status:w'], dtype=object)
In [50]:
loan_data.isnull()
Out[50]:
| id | member_id | loan_amnt | funded_amnt | funded_amnt_inv | term | int_rate | installment | grade | sub_grade | emp_title | emp_length | home_ownership | annual_inc | verification_status | issue_d | loan_status | pymnt_plan | url | desc | purpose | title | zip_code | addr_state | dti | delinq_2yrs | earliest_cr_line | fico_range_low | fico_range_high | inq_last_6mths | mths_since_last_delinq | mths_since_last_record | open_acc | pub_rec | revol_bal | revol_util | total_acc | initial_list_status | out_prncp | out_prncp_inv | total_pymnt | total_pymnt_inv | total_rec_prncp | total_rec_int | total_rec_late_fee | recoveries | collection_recovery_fee | last_pymnt_d | last_pymnt_amnt | next_pymnt_d | last_credit_pull_d | last_fico_range_high | last_fico_range_low | collections_12_mths_ex_med | mths_since_last_major_derog | policy_code | application_type | annual_inc_joint | dti_joint | verification_status_joint | acc_now_delinq | tot_coll_amt | tot_cur_bal | open_acc_6m | open_act_il | open_il_12m | open_il_24m | mths_since_rcnt_il | total_bal_il | il_util | open_rv_12m | open_rv_24m | max_bal_bc | all_util | total_rev_hi_lim | inq_fi | total_cu_tl | inq_last_12m | acc_open_past_24mths | avg_cur_bal | bc_open_to_buy | bc_util | chargeoff_within_12_mths | delinq_amnt | mo_sin_old_il_acct | mo_sin_old_rev_tl_op | mo_sin_rcnt_rev_tl_op | mo_sin_rcnt_tl | mort_acc | mths_since_recent_bc | mths_since_recent_bc_dlq | mths_since_recent_inq | mths_since_recent_revol_delinq | num_accts_ever_120_pd | num_actv_bc_tl | num_actv_rev_tl | num_bc_sats | num_bc_tl | num_il_tl | num_op_rev_tl | num_rev_accts | num_rev_tl_bal_gt_0 | num_sats | num_tl_120dpd_2m | num_tl_30dpd | num_tl_90g_dpd_24m | num_tl_op_past_12m | pct_tl_nvr_dlq | percent_bc_gt_75 | pub_rec_bankruptcies | tax_liens | tot_hi_cred_lim | total_bal_ex_mort | total_bc_limit | total_il_high_credit_limit | revol_bal_joint | sec_app_fico_range_low | sec_app_fico_range_high | sec_app_earliest_cr_line | sec_app_inq_last_6mths | sec_app_mort_acc | sec_app_open_acc | sec_app_revol_util | sec_app_open_act_il | sec_app_num_rev_accts | sec_app_chargeoff_within_12_mths | sec_app_collections_12_mths_ex_med | sec_app_mths_since_last_major_derog | hardship_flag | hardship_type | hardship_reason | hardship_status | deferral_term | hardship_amount | hardship_start_date | hardship_end_date | payment_plan_start_date | hardship_length | hardship_dpd | hardship_loan_status | orig_projected_additional_accrued_interest | hardship_payoff_balance_amount | hardship_last_payment_amount | disbursement_method | debt_settlement_flag | debt_settlement_flag_date | settlement_status | settlement_date | settlement_amount | settlement_percentage | settlement_term | emp_length_int | term_int | earliest_cr_line_date | mth_earliest_cr_line_date | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | False | False | True | True | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | True | True | True | True | True | True | True | True | True | True | True | True | False | True | True | True | True | True | True | True | True | True | True | True | True | True | True | False | False | True | True | True | True | True | True | False | False | False | False |
| 1 | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | True | False | False | True | True | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | True | True | True | True | True | True | True | True | True | True | True | True | False | True | True | True | True | True | True | True | True | True | True | True | True | True | True | False | False | True | True | True | True | True | True | False | False | False | False |
| 2 | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | True | False | False | False | False | False | False | False | False | True | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | True | True | True | True | True | True | True | True | True | True | True | True | False | True | True | True | True | True | True | True | True | True | True | True | True | True | True | False | False | True | True | True | True | True | True | False | False | False | False |
| 3 | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | False | False | False | False | False | True | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | True | True | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | True | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | True | True | True | True | True | True | True | True | True | True | True | True | False | True | True | True | True | True | True | True | True | True | True | True | True | True | True | False | False | True | True | True | True | True | True | False | False | False | False |
| 4 | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | True | False | False | True | True | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | True | True | True | True | True | True | True | True | True | True | True | True | False | True | True | True | True | True | True | True | True | True | True | True | True | True | True | False | False | True | True | True | True | True | True | False | False | False | False |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1048570 | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | False | False | False | False | False | True | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | True | False | False | True | True | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | True | True | True | True | True | True | True | True | True | True | True | True | False | True | True | True | True | True | True | True | True | True | True | True | True | True | True | False | False | False | False | False | False | False | False | False | False | False | False |
| 1048571 | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | True | False | False | True | True | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | True | True | True | True | True | True | True | True | True | True | True | True | False | True | True | True | True | True | True | True | True | True | True | True | True | True | True | False | False | True | True | True | True | True | True | False | False | False | False |
| 1048572 | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | False | False | False | False | False | True | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | True | False | False | True | True | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | True | True | True | True | True | True | True | True | True | True | True | True | False | True | True | True | True | True | True | True | True | True | True | True | True | True | True | False | False | True | True | True | True | True | True | False | False | False | False |
| 1048573 | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | True | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | True | False | False | True | True | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | True | True | True | True | True | True | True | True | True | True | True | True | False | True | True | True | True | True | True | True | True | True | True | True | True | True | True | False | False | True | True | True | True | True | True | False | False | False | False |
| 1048574 | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | True | False | False | True | True | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | True | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | True | True | True | True | True | True | True | True | True | True | True | True | False | True | True | True | True | True | True | True | True | True | True | True | True | True | True | False | False | True | True | True | True | True | True | False | False | False | False |
1048575 rows × 155 columns
In [51]:
pd.options.display.max_rows = None
loan_data.isnull().sum()
Out[51]:
id 0 member_id 1048575 loan_amnt 12 funded_amnt 12 funded_amnt_inv 12 term 12 int_rate 12 installment 12 grade 12 sub_grade 12 emp_title 73491 emp_length 69960 home_ownership 12 annual_inc 12 verification_status 12 issue_d 12 loan_status 12 pymnt_plan 12 url 12 desc 1048518 purpose 12 title 12149 zip_code 12 addr_state 12 dti 868 delinq_2yrs 12 earliest_cr_line 12 fico_range_low 12 fico_range_high 12 inq_last_6mths 12 mths_since_last_delinq 526428 mths_since_last_record 873995 open_acc 12 pub_rec 12 revol_bal 12 revol_util 738 total_acc 12 initial_list_status 12 out_prncp 12 out_prncp_inv 12 total_pymnt 12 total_pymnt_inv 12 total_rec_prncp 12 total_rec_int 12 total_rec_late_fee 12 recoveries 12 collection_recovery_fee 12 last_pymnt_d 1108 last_pymnt_amnt 12 next_pymnt_d 611794 last_credit_pull_d 28 last_fico_range_high 12 last_fico_range_low 12 collections_12_mths_ex_med 12 mths_since_last_major_derog 763087 policy_code 12 application_type 12 annual_inc_joint 990249 dti_joint 990252 verification_status_joint 992619 acc_now_delinq 12 tot_coll_amt 12 tot_cur_bal 12 open_acc_6m 399735 open_act_il 399735 open_il_12m 399735 open_il_24m 399735 mths_since_rcnt_il 419995 total_bal_il 399735 il_util 493449 open_rv_12m 399735 open_rv_24m 399735 max_bal_bc 399735 all_util 399827 total_rev_hi_lim 12 inq_fi 399735 total_cu_tl 399735 inq_last_12m 399735 acc_open_past_24mths 12 avg_cur_bal 41 bc_open_to_buy 11683 bc_util 12235 chargeoff_within_12_mths 12 delinq_amnt 12 mo_sin_old_il_acct 31964 mo_sin_old_rev_tl_op 12 mo_sin_rcnt_rev_tl_op 12 mo_sin_rcnt_tl 12 mort_acc 12 mths_since_recent_bc 11077 mths_since_recent_bc_dlq 797831 mths_since_recent_inq 117025 mths_since_recent_revol_delinq 691583 num_accts_ever_120_pd 12 num_actv_bc_tl 12 num_actv_rev_tl 12 num_bc_sats 12 num_bc_tl 12 num_il_tl 12 num_op_rev_tl 12 num_rev_accts 13 num_rev_tl_bal_gt_0 12 num_sats 12 num_tl_120dpd_2m 44728 num_tl_30dpd 12 num_tl_90g_dpd_24m 12 num_tl_op_past_12m 12 pct_tl_nvr_dlq 14 percent_bc_gt_75 11975 pub_rec_bankruptcies 12 tax_liens 12 tot_hi_cred_lim 12 total_bal_ex_mort 12 total_bc_limit 12 total_il_high_credit_limit 12 revol_bal_joint 993659 sec_app_fico_range_low 993659 sec_app_fico_range_high 993659 sec_app_earliest_cr_line 993659 sec_app_inq_last_6mths 993659 sec_app_mort_acc 993659 sec_app_open_acc 993659 sec_app_revol_util 994561 sec_app_open_act_il 993659 sec_app_num_rev_accts 993659 sec_app_chargeoff_within_12_mths 993659 sec_app_collections_12_mths_ex_med 993659 sec_app_mths_since_last_major_derog 1030351 hardship_flag 12 hardship_type 1042463 hardship_reason 1042463 hardship_status 1042463 deferral_term 1042463 hardship_amount 1042463 hardship_start_date 1042463 hardship_end_date 1042463 payment_plan_start_date 1042463 hardship_length 1042463 hardship_dpd 1042463 hardship_loan_status 1042463 orig_projected_additional_accrued_interest 1043693 hardship_payoff_balance_amount 1042463 hardship_last_payment_amount 1042463 disbursement_method 12 debt_settlement_flag 12 debt_settlement_flag_date 1029866 settlement_status 1029866 settlement_date 1029866 settlement_amount 1029866 settlement_percentage 1029866 settlement_term 1029866 emp_length_int 0 term_int 12 earliest_cr_line_date 12 mth_earliest_cr_line_date 12 dtype: int64
In [52]:
pd.options.display.max_rows = 100
In [53]:
loan_data['total_rev_hi_lim'].fillna(loan_data['funded_amnt'], inplace = True)
C:\Users\computer\AppData\Local\Temp\ipykernel_3596\729504533.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
loan_data['total_rev_hi_lim'].fillna(loan_data['funded_amnt'], inplace = True)
In [54]:
loan_data['total_rev_hi_lim'].isnull().sum()
Out[54]:
12
In [55]:
loan_data['loan_status'].unique()
Out[55]:
array(['Fully Paid', 'Current', 'Charged Off', 'In Grace Period',
'Late (31-120 days)', 'Late (16-30 days)', 'Default', nan],
dtype=object)
In [56]:
loan_data['loan_status'].value_counts()
Out[56]:
loan_status Fully Paid 482595 Current 419550 Charged Off 129187 Late (31-120 days) 10879 In Grace Period 4227 Late (16-30 days) 2104 Default 21 Name: count, dtype: int64
In [57]:
loan_data['loan_status'].value_counts() / loan_data['loan_status'].count()
Out[57]:
loan_status Fully Paid 0.460244 Current 0.400119 Charged Off 0.123204 Late (31-120 days) 0.010375 In Grace Period 0.004031 Late (16-30 days) 0.002007 Default 0.000020 Name: count, dtype: float64
In [58]:
import pandas as pd
# Assuming loan_data is already read from the CSV file
print("Unique values in 'loan_status':")
print(loan_data['loan_status'].unique())
Unique values in 'loan_status': ['Fully Paid' 'Current' 'Charged Off' 'In Grace Period' 'Late (31-120 days)' 'Late (16-30 days)' 'Default' nan]
In [59]:
import numpy as np
# Strip leading/trailing spaces from the 'loan_status' column
loan_data['loan_status'] = loan_data['loan_status'].str.strip()
# Define the 'good_bad' column using np.where
loan_data['good_bad'] = np.where(
loan_data['loan_status'].isin([
'Charged Off',
'Default',
'Late(31-120 days)'
]),
0,
1
)
# Verify the result
print(loan_data[['loan_status', 'good_bad']].head())
print(loan_data['good_bad'].value_counts())
loan_status good_bad 0 Fully Paid 1 1 Fully Paid 1 2 Fully Paid 1 3 Current 1 4 Fully Paid 1 good_bad 1 919367 0 129208 Name: count, dtype: int64
In [60]:
loan_data['good_bad']
Out[60]:
0 1
1 1
2 1
3 1
4 1
..
1048570 0
1048571 1
1048572 1
1048573 1
1048574 1
Name: good_bad, Length: 1048575, dtype: int32
In [61]:
from sklearn.model_selection import train_test_split
In [62]:
train_test_split(loan_data.drop('good_bad', axis = 1),loan_data['good_bad'])
Out[62]:
[ id member_id loan_amnt funded_amnt funded_amnt_inv \
583729 116272164 NaN 6000.0 6000.0 6000.0
833277 137839149 NaN 10000.0 10000.0 10000.0
87186 61438967 NaN 24000.0 24000.0 24000.0
185251 55909665 NaN 15000.0 15000.0 15000.0
165960 58623061 NaN 25000.0 25000.0 25000.0
... ... ... ... ... ...
752960 140944216 NaN 30000.0 30000.0 30000.0
335602 43946147 NaN 6250.0 6250.0 6250.0
608791 114954560 NaN 12000.0 12000.0 12000.0
421739 130943290 NaN 13000.0 13000.0 13000.0
314591 46346960 NaN 7000.0 7000.0 7000.0
term int_rate installment grade sub_grade \
583729 36 months 13.59 203.88 C C2
833277 36 months 14.47 344.07 C C2
87186 36 months 5.32 722.76 A A1
185251 36 months 6.24 457.97 A A2
165960 60 months 9.99 531.06 B B3
... ... ... ... ... ...
752960 60 months 11.55 660.54 B B4
335602 36 months 12.29 208.46 C C1
608791 36 months 7.21 371.68 A A3
421739 36 months 11.98 431.67 B B5
314591 36 months 6.39 214.20 A A2
emp_title emp_length home_ownership annual_inc \
583729 switcher 10+ years MORTGAGE 40000.0
833277 Hotel Ops Scheduler 5 years RENT 55000.0
87186 professor 8 years MORTGAGE 99000.0
185251 Teacher 8 years MORTGAGE 60000.0
165960 HR Advisor 10+ years RENT 90000.0
... ... ... ... ...
752960 Superintendent 10+ years MORTGAGE 98000.0
335602 Driver 1st shift overstock 3 years RENT 26000.0
608791 Assistant manager 10+ years MORTGAGE 75302.0
421739 Office Manager / Paralegal 10+ years MORTGAGE 56000.0
314591 service Manager 10+ years MORTGAGE 90000.0
verification_status issue_d loan_status pymnt_plan \
583729 Source Verified Aug-17 Current n
833277 Source Verified Aug-18 Current n
87186 Not Verified Oct-15 Fully Paid n
185251 Not Verified Aug-15 Fully Paid n
165960 Not Verified Aug-15 Current n
... ... ... ... ...
752960 Source Verified Sep-18 Current n
335602 Not Verified Apr-15 Fully Paid n
608791 Not Verified Aug-17 Current n
421739 Verified Mar-18 Fully Paid n
314591 Verified Apr-15 Fully Paid n
url desc \
583729 https://lendingclub.com/browse/loanDetail.acti... NaN
833277 https://lendingclub.com/browse/loanDetail.acti... NaN
87186 https://lendingclub.com/browse/loanDetail.acti... NaN
185251 https://lendingclub.com/browse/loanDetail.acti... NaN
165960 https://lendingclub.com/browse/loanDetail.acti... NaN
... ... ...
752960 https://lendingclub.com/browse/loanDetail.acti... NaN
335602 https://lendingclub.com/browse/loanDetail.acti... NaN
608791 https://lendingclub.com/browse/loanDetail.acti... NaN
421739 https://lendingclub.com/browse/loanDetail.acti... NaN
314591 https://lendingclub.com/browse/loanDetail.acti... NaN
purpose title zip_code addr_state \
583729 home_improvement Home improvement 281xx NC
833277 debt_consolidation Debt consolidation 534xx WI
87186 home_improvement Home improvement 312xx GA
185251 debt_consolidation Debt consolidation 080xx NJ
165960 credit_card Credit card refinancing 331xx FL
... ... ... ... ...
752960 other Other 932xx CA
335602 debt_consolidation Debt consolidation 660xx KS
608791 credit_card Credit card refinancing 231xx VA
421739 debt_consolidation Debt consolidation 846xx UT
314591 home_improvement Home improvement 953xx CA
dti delinq_2yrs earliest_cr_line fico_range_low fico_range_high \
583729 20.01 0.0 Feb-97 680.0 684.0
833277 31.81 0.0 Mar-01 680.0 684.0
87186 27.38 0.0 Feb-95 725.0 729.0
185251 9.62 0.0 Feb-03 755.0 759.0
165960 16.24 0.0 May-00 700.0 704.0
... ... ... ... ... ...
752960 17.54 0.0 Sep-03 745.0 749.0
335602 20.59 0.0 Jun-07 725.0 729.0
608791 18.28 0.0 Aug-01 705.0 709.0
421739 28.23 0.0 May-90 700.0 704.0
314591 7.77 0.0 Jan-92 815.0 819.0
inq_last_6mths mths_since_last_delinq mths_since_last_record \
583729 0.0 54.0 NaN
833277 0.0 NaN NaN
87186 0.0 NaN NaN
185251 0.0 40.0 NaN
165960 0.0 NaN NaN
... ... ... ...
752960 1.0 NaN NaN
335602 1.0 NaN NaN
608791 0.0 50.0 NaN
421739 0.0 NaN 97.0
314591 0.0 NaN NaN
open_acc pub_rec revol_bal revol_util total_acc \
583729 4.0 0.0 1465.0 77.1 20.0
833277 18.0 0.0 43420.0 89.2 25.0
87186 27.0 0.0 21912.0 28.8 56.0
185251 12.0 0.0 9983.0 21.3 30.0
165960 10.0 0.0 29960.0 60.0 13.0
... ... ... ... ... ...
752960 7.0 0.0 13310.0 51.8 14.0
335602 8.0 0.0 4934.0 22.0 9.0
608791 15.0 0.0 14860.0 48.6 29.0
421739 24.0 1.0 11551.0 34.3 62.0
314591 14.0 0.0 15266.0 16.4 29.0
initial_list_status out_prncp out_prncp_inv total_pymnt \
583729 w 3136.41 3136.41 3864.660000
833277 w 8377.85 8377.85 2400.450000
87186 w 0.00 0.00 26038.487570
185251 f 0.00 0.00 16376.701420
165960 w 8385.37 8385.37 22796.140000
... ... ... ... ...
752960 w 27714.89 27714.89 3943.990000
335602 f 0.00 0.00 7500.124972
608791 w 5989.45 5989.45 7057.110000
421739 w 0.00 0.00 13657.565240
314591 f 0.00 0.00 7407.160001
total_pymnt_inv total_rec_prncp total_rec_int total_rec_late_fee \
583729 3864.66 2863.59 1001.07 0.0
833277 2400.45 1622.15 778.30 0.0
87186 26038.49 24000.00 2038.49 0.0
185251 16376.70 15000.00 1376.70 0.0
165960 22796.14 16614.63 6181.51 0.0
... ... ... ... ...
752960 3943.99 2285.11 1658.88 0.0
335602 7500.12 6250.00 1250.12 0.0
608791 7057.11 6010.55 1046.56 0.0
421739 13657.57 13000.00 657.57 0.0
314591 7407.16 7000.00 407.16 0.0
recoveries collection_recovery_fee last_pymnt_d last_pymnt_amnt \
583729 0.0 0.0 Mar-19 203.88
833277 0.0 0.0 Mar-19 344.07
87186 0.0 0.0 Dec-18 1.40
185251 0.0 0.0 Sep-17 5359.42
165960 0.0 0.0 Mar-19 531.06
... ... ... ... ...
752960 0.0 0.0 Mar-19 660.54
335602 0.0 0.0 Apr-18 208.29
608791 0.0 0.0 Mar-19 371.68
421739 0.0 0.0 Nov-18 7076.79
314591 0.0 0.0 Jun-16 4839.24
next_pymnt_d last_credit_pull_d last_fico_range_high \
583729 Apr-19 Mar-19 674.0
833277 Apr-19 Mar-19 664.0
87186 NaN Mar-19 729.0
185251 NaN Oct-17 779.0
165960 Apr-19 Mar-19 629.0
... ... ... ...
752960 Apr-19 Mar-19 729.0
335602 NaN May-18 589.0
608791 Apr-19 Mar-19 694.0
421739 NaN Mar-19 719.0
314591 NaN May-16 799.0
last_fico_range_low collections_12_mths_ex_med \
583729 670.0 0.0
833277 660.0 0.0
87186 725.0 0.0
185251 775.0 0.0
165960 625.0 0.0
... ... ...
752960 725.0 0.0
335602 585.0 0.0
608791 690.0 0.0
421739 715.0 0.0
314591 795.0 0.0
mths_since_last_major_derog policy_code application_type \
583729 54.0 1.0 Individual
833277 NaN 1.0 Individual
87186 NaN 1.0 Individual
185251 NaN 1.0 Individual
165960 NaN 1.0 Individual
... ... ... ...
752960 NaN 1.0 Individual
335602 NaN 1.0 Individual
608791 50.0 1.0 Individual
421739 NaN 1.0 Individual
314591 NaN 1.0 Individual
annual_inc_joint dti_joint verification_status_joint acc_now_delinq \
583729 NaN NaN NaN 0.0
833277 NaN NaN NaN 0.0
87186 NaN NaN NaN 0.0
185251 NaN NaN NaN 0.0
165960 NaN NaN NaN 0.0
... ... ... ... ...
752960 NaN NaN NaN 0.0
335602 NaN NaN NaN 0.0
608791 NaN NaN NaN 0.0
421739 NaN NaN NaN 0.0
314591 NaN NaN NaN 0.0
tot_coll_amt tot_cur_bal open_acc_6m open_act_il open_il_12m \
583729 0.0 60227.0 1.0 1.0 1.0
833277 0.0 52759.0 0.0 1.0 0.0
87186 0.0 213714.0 NaN NaN NaN
185251 0.0 254051.0 NaN NaN NaN
165960 0.0 33760.0 NaN NaN NaN
... ... ... ... ... ...
752960 0.0 207193.0 1.0 2.0 1.0
335602 0.0 10668.0 NaN NaN NaN
608791 0.0 206413.0 1.0 2.0 0.0
421739 0.0 92821.0 5.0 13.0 5.0
314591 0.0 474972.0 NaN NaN NaN
open_il_24m mths_since_rcnt_il total_bal_il il_util open_rv_12m \
583729 1.0 6.0 10337.0 95.0 1.0
833277 0.0 38.0 9339.0 50.0 0.0
87186 NaN NaN NaN NaN NaN
185251 NaN NaN NaN NaN NaN
165960 NaN NaN NaN NaN NaN
... ... ... ... ... ...
752960 3.0 5.0 39746.0 82.0 0.0
335602 NaN NaN NaN NaN NaN
608791 1.0 20.0 36224.0 66.0 1.0
421739 6.0 4.0 81270.0 102.0 4.0
314591 NaN NaN NaN NaN NaN
open_rv_24m max_bal_bc all_util total_rev_hi_lim inq_fi \
583729 2.0 1277.0 92.0 1900.0 0.0
833277 2.0 7272.0 78.0 48700.0 0.0
87186 NaN NaN NaN 76000.0 NaN
185251 NaN NaN NaN 46800.0 NaN
165960 NaN NaN NaN 49900.0 NaN
... ... ... ... ... ...
752960 1.0 12786.0 72.0 25700.0 2.0
335602 NaN NaN NaN 22400.0 NaN
608791 2.0 4074.0 59.0 30600.0 1.0
421739 7.0 5985.0 82.0 33700.0 2.0
314591 NaN NaN NaN 93100.0 NaN
total_cu_tl inq_last_12m acc_open_past_24mths avg_cur_bal \
583729 3.0 0.0 3.0 15057.0
833277 1.0 0.0 2.0 2931.0
87186 NaN NaN 4.0 8549.0
185251 NaN NaN 3.0 25405.0
165960 NaN NaN 3.0 3376.0
... ... ... ... ...
752960 2.0 1.0 4.0 29599.0
335602 NaN NaN 4.0 1524.0
608791 1.0 1.0 4.0 15878.0
421739 10.0 3.0 13.0 4036.0
314591 NaN NaN 4.0 36536.0
bc_open_to_buy bc_util chargeoff_within_12_mths delinq_amnt \
583729 223.0 85.1 0.0 0.0
833277 2748.0 93.6 0.0 0.0
87186 29530.0 38.6 0.0 0.0
185251 35817.0 21.8 0.0 0.0
165960 16440.0 63.0 0.0 0.0
... ... ... ... ...
752960 10190.0 56.6 0.0 0.0
335602 7176.0 25.2 0.0 0.0
608791 10321.0 56.5 0.0 0.0
421739 13073.0 43.7 0.0 0.0
314591 62038.0 19.5 0.0 0.0
mo_sin_old_il_acct mo_sin_old_rev_tl_op mo_sin_rcnt_rev_tl_op \
583729 169.0 246.0 8.0
833277 190.0 208.0 24.0
87186 239.0 248.0 11.0
185251 142.0 149.0 18.0
165960 156.0 183.0 2.0
... ... ... ...
752960 138.0 152.0 16.0
335602 93.0 77.0 3.0
608791 144.0 191.0 12.0
421739 149.0 334.0 2.0
314591 137.0 279.0 16.0
mo_sin_rcnt_tl mort_acc mths_since_recent_bc \
583729 6.0 2.0 18.0
833277 24.0 0.0 24.0
87186 11.0 4.0 25.0
185251 13.0 2.0 18.0
165960 2.0 0.0 2.0
... ... ... ...
752960 5.0 1.0 16.0
335602 3.0 0.0 3.0
608791 5.0 4.0 12.0
421739 2.0 0.0 2.0
314591 8.0 2.0 16.0
mths_since_recent_bc_dlq mths_since_recent_inq \
583729 NaN 18.0
833277 NaN NaN
87186 NaN 11.0
185251 NaN NaN
165960 NaN NaN
... ... ...
752960 NaN 5.0
335602 NaN 3.0
608791 NaN 7.0
421739 NaN 5.0
314591 NaN 20.0
mths_since_recent_revol_delinq num_accts_ever_120_pd num_actv_bc_tl \
583729 54.0 5.0 1.0
833277 NaN 0.0 16.0
87186 NaN 1.0 4.0
185251 40.0 0.0 4.0
165960 NaN 0.0 7.0
... ... ... ...
752960 NaN 0.0 2.0
335602 NaN 0.0 1.0
608791 NaN 2.0 4.0
421739 NaN 0.0 4.0
314591 NaN 0.0 3.0
num_actv_rev_tl num_bc_sats num_bc_tl num_il_tl num_op_rev_tl \
583729 2.0 1.0 2.0 11.0 2.0
833277 17.0 16.0 18.0 5.0 17.0
87186 7.0 6.0 12.0 20.0 17.0
185251 4.0 6.0 10.0 13.0 9.0
165960 8.0 8.0 10.0 2.0 9.0
... ... ... ... ... ...
752960 2.0 2.0 2.0 6.0 4.0
335602 2.0 2.0 2.0 2.0 7.0
608791 6.0 5.0 9.0 7.0 12.0
421739 6.0 5.0 15.0 37.0 11.0
314591 4.0 6.0 9.0 4.0 12.0
num_rev_accts num_rev_tl_bal_gt_0 num_sats num_tl_120dpd_2m \
583729 7.0 2.0 4.0 0.0
833277 20.0 17.0 18.0 0.0
87186 32.0 8.0 27.0 0.0
185251 15.0 4.0 12.0 0.0
165960 11.0 8.0 10.0 0.0
... ... ... ... ...
752960 7.0 2.0 7.0 0.0
335602 7.0 2.0 8.0 0.0
608791 18.0 6.0 15.0 0.0
421739 25.0 6.0 24.0 0.0
314591 22.0 4.0 14.0 0.0
num_tl_30dpd num_tl_90g_dpd_24m num_tl_op_past_12m pct_tl_nvr_dlq \
583729 0.0 0.0 2.0 75.0
833277 0.0 0.0 0.0 100.0
87186 0.0 0.0 2.0 98.2
185251 0.0 0.0 0.0 96.7
165960 0.0 0.0 3.0 100.0
... ... ... ... ...
752960 0.0 0.0 1.0 100.0
335602 0.0 0.0 3.0 100.0
608791 0.0 0.0 2.0 93.1
421739 0.0 0.0 9.0 100.0
314591 0.0 0.0 1.0 100.0
percent_bc_gt_75 pub_rec_bankruptcies tax_liens tot_hi_cred_lim \
583729 100.0 0.0 0.0 73987.0
833277 93.8 0.0 0.0 67286.0
87186 0.0 0.0 0.0 315684.0
185251 16.7 0.0 0.0 318861.0
165960 50.0 0.0 0.0 72114.0
... ... ... ... ...
752960 50.0 0.0 0.0 300718.0
335602 0.0 0.0 0.0 29875.0
608791 50.0 0.0 0.0 241962.0
421739 20.0 1.0 0.0 113367.0
314591 0.0 0.0 0.0 579556.0
total_bal_ex_mort total_bc_limit total_il_high_credit_limit \
583729 11802.0 1500.0 10935.0
833277 52759.0 43200.0 18586.0
87186 87677.0 48100.0 99275.0
185251 37593.0 45800.0 29061.0
165960 33760.0 44400.0 22214.0
... ... ... ...
752960 53056.0 23500.0 48453.0
335602 10668.0 9600.0 7475.0
608791 51084.0 23700.0 55341.0
421739 92821.0 23200.0 79667.0
314591 39235.0 77100.0 25994.0
revol_bal_joint sec_app_fico_range_low sec_app_fico_range_high \
583729 NaN NaN NaN
833277 NaN NaN NaN
87186 NaN NaN NaN
185251 NaN NaN NaN
165960 NaN NaN NaN
... ... ... ...
752960 NaN NaN NaN
335602 NaN NaN NaN
608791 NaN NaN NaN
421739 NaN NaN NaN
314591 NaN NaN NaN
sec_app_earliest_cr_line sec_app_inq_last_6mths sec_app_mort_acc \
583729 NaN NaN NaN
833277 NaN NaN NaN
87186 NaN NaN NaN
185251 NaN NaN NaN
165960 NaN NaN NaN
... ... ... ...
752960 NaN NaN NaN
335602 NaN NaN NaN
608791 NaN NaN NaN
421739 NaN NaN NaN
314591 NaN NaN NaN
sec_app_open_acc sec_app_revol_util sec_app_open_act_il \
583729 NaN NaN NaN
833277 NaN NaN NaN
87186 NaN NaN NaN
185251 NaN NaN NaN
165960 NaN NaN NaN
... ... ... ...
752960 NaN NaN NaN
335602 NaN NaN NaN
608791 NaN NaN NaN
421739 NaN NaN NaN
314591 NaN NaN NaN
sec_app_num_rev_accts sec_app_chargeoff_within_12_mths \
583729 NaN NaN
833277 NaN NaN
87186 NaN NaN
185251 NaN NaN
165960 NaN NaN
... ... ...
752960 NaN NaN
335602 NaN NaN
608791 NaN NaN
421739 NaN NaN
314591 NaN NaN
sec_app_collections_12_mths_ex_med \
583729 NaN
833277 NaN
87186 NaN
185251 NaN
165960 NaN
... ...
752960 NaN
335602 NaN
608791 NaN
421739 NaN
314591 NaN
sec_app_mths_since_last_major_derog hardship_flag hardship_type \
583729 NaN N NaN
833277 NaN N NaN
87186 NaN N NaN
185251 NaN N NaN
165960 NaN N NaN
... ... ... ...
752960 NaN N NaN
335602 NaN N NaN
608791 NaN N NaN
421739 NaN N NaN
314591 NaN N NaN
hardship_reason hardship_status deferral_term hardship_amount \
583729 NaN NaN NaN NaN
833277 NaN NaN NaN NaN
87186 NaN NaN NaN NaN
185251 NaN NaN NaN NaN
165960 NaN NaN NaN NaN
... ... ... ... ...
752960 NaN NaN NaN NaN
335602 NaN NaN NaN NaN
608791 NaN NaN NaN NaN
421739 NaN NaN NaN NaN
314591 NaN NaN NaN NaN
hardship_start_date hardship_end_date payment_plan_start_date \
583729 NaN NaN NaN
833277 NaN NaN NaN
87186 NaN NaN NaN
185251 NaN NaN NaN
165960 NaN NaN NaN
... ... ... ...
752960 NaN NaN NaN
335602 NaN NaN NaN
608791 NaN NaN NaN
421739 NaN NaN NaN
314591 NaN NaN NaN
hardship_length hardship_dpd hardship_loan_status \
583729 NaN NaN NaN
833277 NaN NaN NaN
87186 NaN NaN NaN
185251 NaN NaN NaN
165960 NaN NaN NaN
... ... ... ...
752960 NaN NaN NaN
335602 NaN NaN NaN
608791 NaN NaN NaN
421739 NaN NaN NaN
314591 NaN NaN NaN
orig_projected_additional_accrued_interest \
583729 NaN
833277 NaN
87186 NaN
185251 NaN
165960 NaN
... ...
752960 NaN
335602 NaN
608791 NaN
421739 NaN
314591 NaN
hardship_payoff_balance_amount hardship_last_payment_amount \
583729 NaN NaN
833277 NaN NaN
87186 NaN NaN
185251 NaN NaN
165960 NaN NaN
... ... ...
752960 NaN NaN
335602 NaN NaN
608791 NaN NaN
421739 NaN NaN
314591 NaN NaN
disbursement_method debt_settlement_flag debt_settlement_flag_date \
583729 Cash N NaN
833277 Cash N NaN
87186 Cash N NaN
185251 Cash N NaN
165960 Cash N NaN
... ... ... ...
752960 Cash N NaN
335602 Cash N NaN
608791 Cash N NaN
421739 Cash N NaN
314591 Cash N NaN
settlement_status settlement_date settlement_amount \
583729 NaN NaN NaN
833277 NaN NaN NaN
87186 NaN NaN NaN
185251 NaN NaN NaN
165960 NaN NaN NaN
... ... ... ...
752960 NaN NaN NaN
335602 NaN NaN NaN
608791 NaN NaN NaN
421739 NaN NaN NaN
314591 NaN NaN NaN
settlement_percentage settlement_term emp_length_int term_int \
583729 NaN NaN 10.0 36.0
833277 NaN NaN 5.0 36.0
87186 NaN NaN 8.0 36.0
185251 NaN NaN 8.0 36.0
165960 NaN NaN 10.0 60.0
... ... ... ... ...
752960 NaN NaN 10.0 60.0
335602 NaN NaN 3.0 36.0
608791 NaN NaN 10.0 36.0
421739 NaN NaN 10.0 36.0
314591 NaN NaN 10.0 36.0
earliest_cr_line_date mth_earliest_cr_line_date
583729 1997-02-01 338.0
833277 2001-03-01 289.0
87186 1995-02-01 363.0
185251 2003-02-01 265.0
165960 2000-05-01 299.0
... ... ...
752960 2003-09-01 258.0
335602 2007-06-01 213.0
608791 2001-08-01 284.0
421739 1990-05-01 421.0
314591 1992-01-01 400.0
[786431 rows x 155 columns],
id member_id loan_amnt funded_amnt funded_amnt_inv \
896093 110977877 NaN 17200.0 17200.0 17200.0
668508 81577525 NaN 10000.0 10000.0 10000.0
406913 38559264 NaN 6500.0 6500.0 6500.0
233509 53864145 NaN 12000.0 12000.0 12000.0
425880 129585507 NaN 40000.0 40000.0 40000.0
... ... ... ... ... ...
929582 109271124 NaN 12000.0 12000.0 12000.0
248545 52077214 NaN 10000.0 10000.0 10000.0
918781 108235068 NaN 6000.0 6000.0 6000.0
251703 52017154 NaN 22000.0 22000.0 22000.0
762846 140639152 NaN 17000.0 17000.0 17000.0
term int_rate installment grade sub_grade \
896093 36 months 21.45 652.00 D D5
668508 36 months 11.99 332.10 C C1
406913 36 months 12.99 218.98 C C2
233509 36 months 13.99 410.08 C C4
425880 60 months 14.07 932.19 C C3
... ... ... ... ... ...
929582 60 months 17.09 298.82 D D1
248545 36 months 13.33 338.54 C C3
918781 36 months 7.97 187.94 A A5
251703 36 months 6.24 671.68 A A2
762846 36 months 7.84 531.47 A A4
emp_title emp_length home_ownership annual_inc \
896093 Assistant Director 10+ years MORTGAGE 117480.0
668508 Payroll Specialist 6 years MORTGAGE 48000.0
406913 Accounting 10+ years RENT 25200.0
233509 Registered Nurse 8 years RENT 90000.0
425880 NaN NaN OWN 75000.0
... ... ... ... ...
929582 Office Manager/RDA 10+ years MORTGAGE 41000.0
248545 NaN NaN RENT 24618.0
918781 Production worker < 1 year MORTGAGE 72800.0
251703 Network Engineer 5 years MORTGAGE 113000.0
762846 Manufacturing Operator < 1 year MORTGAGE 50000.0
verification_status issue_d loan_status pymnt_plan \
896093 Verified Jun-17 Current n
668508 Verified Jun-16 Fully Paid n
406913 Source Verified Jan-15 Fully Paid n
233509 Source Verified Jul-15 Fully Paid n
425880 Source Verified Mar-18 Current n
... ... ... ... ...
929582 Source Verified May-17 Current n
248545 Verified Jun-15 Fully Paid n
918781 Source Verified May-17 Fully Paid n
251703 Source Verified Jun-15 Fully Paid n
762846 Source Verified Sep-18 Current n
url desc \
896093 https://lendingclub.com/browse/loanDetail.acti... NaN
668508 https://lendingclub.com/browse/loanDetail.acti... NaN
406913 https://lendingclub.com/browse/loanDetail.acti... NaN
233509 https://lendingclub.com/browse/loanDetail.acti... NaN
425880 https://lendingclub.com/browse/loanDetail.acti... NaN
... ... ...
929582 https://lendingclub.com/browse/loanDetail.acti... NaN
248545 https://lendingclub.com/browse/loanDetail.acti... NaN
918781 https://lendingclub.com/browse/loanDetail.acti... NaN
251703 https://lendingclub.com/browse/loanDetail.acti... NaN
762846 https://lendingclub.com/browse/loanDetail.acti... NaN
purpose title zip_code addr_state \
896093 debt_consolidation Debt consolidation 301xx GA
668508 debt_consolidation Debt consolidation 282xx NC
406913 debt_consolidation Debt consolidation 705xx LA
233509 other Other 309xx GA
425880 credit_card Credit card refinancing 170xx PA
... ... ... ... ...
929582 debt_consolidation Debt consolidation 922xx CA
248545 credit_card Credit card refinancing 104xx NY
918781 major_purchase Major purchase 456xx OH
251703 debt_consolidation Debt consolidation 786xx TX
762846 debt_consolidation Debt consolidation 286xx NC
dti delinq_2yrs earliest_cr_line fico_range_low fico_range_high \
896093 26.46 0.0 Oct-03 690.0 694.0
668508 16.08 1.0 Apr-02 700.0 704.0
406913 28.38 0.0 Feb-03 670.0 674.0
233509 13.53 0.0 Feb-01 695.0 699.0
425880 17.22 0.0 Apr-82 730.0 734.0
... ... ... ... ... ...
929582 21.46 0.0 Jun-05 665.0 669.0
248545 14.11 0.0 Oct-89 705.0 709.0
918781 2.67 0.0 Sep-01 680.0 684.0
251703 12.24 0.0 Jan-93 765.0 769.0
762846 23.07 0.0 Oct-11 770.0 774.0
inq_last_6mths mths_since_last_delinq mths_since_last_record \
896093 0.0 NaN NaN
668508 0.0 16.0 NaN
406913 0.0 NaN 78.0
233509 1.0 38.0 NaN
425880 0.0 NaN NaN
... ... ... ...
929582 0.0 NaN 84.0
248545 3.0 34.0 NaN
918781 1.0 24.0 47.0
251703 3.0 NaN NaN
762846 0.0 NaN NaN
open_acc pub_rec revol_bal revol_util total_acc \
896093 19.0 0.0 15655.0 43.9 34.0
668508 7.0 0.0 6785.0 54.3 16.0
406913 10.0 2.0 6824.0 62.0 21.0
233509 13.0 0.0 8972.0 36.3 32.0
425880 10.0 0.0 88641.0 30.0 21.0
... ... ... ... ... ...
929582 13.0 1.0 11638.0 65.0 17.0
248545 17.0 0.0 10226.0 23.1 60.0
918781 4.0 1.0 4653.0 46.5 19.0
251703 19.0 0.0 17484.0 22.6 33.0
762846 12.0 0.0 5533.0 14.2 16.0
initial_list_status out_prncp out_prncp_inv total_pymnt \
896093 w 8512.15 8512.15 13651.010000
668508 w 0.00 0.00 11874.186330
406913 w 0.00 0.00 7501.070000
233509 f 0.00 0.00 14720.659730
425880 w 34595.44 34595.44 10251.470000
... ... ... ... ...
929582 w 8722.12 8722.12 6581.410000
248545 f 0.00 0.00 11872.064950
918781 w 0.00 0.00 6319.891124
251703 w 0.00 0.00 24105.348570
762846 w 14022.87 14022.87 3548.100000
total_pymnt_inv total_rec_prncp total_rec_int total_rec_late_fee \
896093 13651.01 8687.85 4963.16 0.0
668508 11874.19 10000.00 1874.19 0.0
406913 7501.07 6500.00 1001.07 0.0
233509 14720.66 12000.00 2720.66 0.0
425880 10251.47 5404.56 4846.91 0.0
... ... ... ... ...
929582 6581.41 3277.88 3303.53 0.0
248545 11872.06 10000.00 1872.06 0.0
918781 6319.89 6000.00 319.89 0.0
251703 24105.35 22000.00 2105.35 0.0
762846 3548.10 2977.13 570.97 0.0
recoveries collection_recovery_fee last_pymnt_d last_pymnt_amnt \
896093 0.0 0.0 Mar-19 652.00
668508 0.0 0.0 Nov-18 2582.05
406913 0.0 0.0 Jun-16 3976.25
233509 0.0 0.0 Jan-18 2781.19
425880 0.0 0.0 Mar-19 932.19
... ... ... ... ...
929582 0.0 0.0 Mar-19 298.82
248545 0.0 0.0 Aug-17 3033.19
918781 0.0 0.0 Feb-18 4837.62
251703 0.0 0.0 Oct-17 6592.10
762846 0.0 0.0 Mar-19 531.47
next_pymnt_d last_credit_pull_d last_fico_range_high \
896093 Apr-19 Mar-19 659.0
668508 NaN Nov-18 739.0
406913 NaN Jul-16 674.0
233509 NaN Sep-18 629.0
425880 Apr-19 Mar-19 709.0
... ... ... ...
929582 Apr-19 Mar-19 694.0
248545 NaN Jan-19 649.0
918781 NaN Mar-19 694.0
251703 NaN Oct-17 734.0
762846 Apr-19 Mar-19 779.0
last_fico_range_low collections_12_mths_ex_med \
896093 655.0 0.0
668508 735.0 0.0
406913 670.0 0.0
233509 625.0 0.0
425880 705.0 0.0
... ... ...
929582 690.0 0.0
248545 645.0 0.0
918781 690.0 0.0
251703 730.0 0.0
762846 775.0 0.0
mths_since_last_major_derog policy_code application_type \
896093 NaN 1.0 Individual
668508 NaN 1.0 Individual
406913 NaN 1.0 Individual
233509 NaN 1.0 Individual
425880 NaN 1.0 Joint App
... ... ... ...
929582 NaN 1.0 Individual
248545 NaN 1.0 Individual
918781 57.0 1.0 Individual
251703 NaN 1.0 Individual
762846 NaN 1.0 Individual
annual_inc_joint dti_joint verification_status_joint acc_now_delinq \
896093 NaN NaN NaN 0.0
668508 NaN NaN NaN 0.0
406913 NaN NaN NaN 0.0
233509 NaN NaN NaN 0.0
425880 131000.0 20.33 Source Verified 0.0
... ... ... ... ...
929582 NaN NaN NaN 0.0
248545 NaN NaN NaN 0.0
918781 NaN NaN NaN 0.0
251703 NaN NaN NaN 0.0
762846 NaN NaN NaN 0.0
tot_coll_amt tot_cur_bal open_acc_6m open_act_il open_il_12m \
896093 0.0 173658.0 1.0 5.0 3.0
668508 0.0 99390.0 2.0 1.0 1.0
406913 0.0 25187.0 NaN NaN NaN
233509 2377.0 84718.0 NaN NaN NaN
425880 0.0 93998.0 1.0 2.0 0.0
... ... ... ... ... ...
929582 0.0 183797.0 0.0 1.0 0.0
248545 333.0 10226.0 NaN NaN NaN
918781 0.0 165796.0 2.0 0.0 0.0
251703 0.0 200544.0 NaN NaN NaN
762846 0.0 126740.0 0.0 1.0 1.0
open_il_24m mths_since_rcnt_il total_bal_il il_util open_rv_12m \
896093 3.0 3.0 71663.0 82.0 1.0
668508 1.0 2.0 13085.0 94.0 1.0
406913 NaN NaN NaN NaN NaN
233509 NaN NaN NaN NaN NaN
425880 3.0 13.0 5357.0 55.0 2.0
... ... ... ... ... ...
929582 0.0 27.0 10653.0 66.0 1.0
248545 NaN NaN NaN NaN NaN
918781 0.0 105.0 0.0 NaN 2.0
251703 NaN NaN NaN NaN NaN
762846 1.0 12.0 18086.0 80.0 1.0
open_rv_24m max_bal_bc all_util total_rev_hi_lim inq_fi \
896093 3.0 5887.0 67.0 35700.0 1.0
668508 2.0 1885.0 75.0 12500.0 1.0
406913 NaN NaN NaN 11000.0 NaN
233509 NaN NaN NaN 24700.0 NaN
425880 3.0 8606.0 34.0 122000.0 2.0
... ... ... ... ... ...
929582 5.0 2446.0 66.0 17900.0 2.0
248545 NaN NaN NaN 42100.0 NaN
918781 2.0 4179.0 47.0 10000.0 3.0
251703 NaN NaN NaN 77500.0 NaN
762846 2.0 1994.0 38.0 39100.0 1.0
total_cu_tl inq_last_12m acc_open_past_24mths avg_cur_bal \
896093 1.0 1.0 7.0 10854.0
668508 0.0 0.0 3.0 14199.0
406913 NaN NaN 6.0 2799.0
233509 NaN NaN 5.0 6517.0
425880 5.0 2.0 7.0 9400.0
... ... ... ... ...
929582 0.0 0.0 6.0 15316.0
248545 NaN NaN 7.0 787.0
918781 0.0 2.0 3.0 41449.0
251703 NaN NaN 5.0 10555.0
762846 0.0 1.0 3.0 10562.0
bc_open_to_buy bc_util chargeoff_within_12_mths delinq_amnt \
896093 7345.0 68.1 0.0 0.0
668508 1517.0 56.7 0.0 0.0
406913 240.0 94.7 0.0 0.0
233509 1529.0 85.0 0.0 0.0
425880 17403.0 38.3 0.0 0.0
... ... ... ... ...
929582 145.0 97.1 0.0 0.0
248545 18764.0 31.8 0.0 0.0
918781 5347.0 46.5 0.0 0.0
251703 34566.0 32.5 0.0 0.0
762846 18828.0 10.3 0.0 0.0
mo_sin_old_il_acct mo_sin_old_rev_tl_op mo_sin_rcnt_rev_tl_op \
896093 164.0 158.0 7.0
668508 170.0 155.0 3.0
406913 108.0 143.0 10.0
233509 149.0 58.0 0.0
425880 138.0 431.0 3.0
... ... ... ...
929582 120.0 143.0 12.0
248545 75.0 308.0 1.0
918781 164.0 188.0 6.0
251703 214.0 269.0 2.0
762846 12.0 83.0 10.0
mo_sin_rcnt_tl mort_acc mths_since_recent_bc \
896093 3.0 3.0 21.0
668508 2.0 4.0 60.0
406913 10.0 0.0 15.0
233509 0.0 1.0 2.0
425880 3.0 2.0 20.0
... ... ... ...
929582 12.0 1.0 23.0
248545 1.0 0.0 3.0
918781 6.0 3.0 6.0
251703 2.0 1.0 2.0
762846 10.0 1.0 23.0
mths_since_recent_bc_dlq mths_since_recent_inq \
896093 NaN 7.0
668508 16.0 21.0
406913 NaN 8.0
233509 NaN 2.0
425880 NaN 1.0
... ... ...
929582 NaN 19.0
248545 34.0 1.0
918781 37.0 6.0
251703 NaN 2.0
762846 NaN 12.0
mths_since_recent_revol_delinq num_accts_ever_120_pd num_actv_bc_tl \
896093 NaN 0.0 5.0
668508 16.0 0.0 2.0
406913 NaN 0.0 2.0
233509 NaN 0.0 4.0
425880 NaN 0.0 2.0
... ... ... ...
929582 NaN 0.0 2.0
248545 34.0 0.0 5.0
918781 37.0 1.0 3.0
251703 NaN 0.0 4.0
762846 NaN 0.0 2.0
num_actv_rev_tl num_bc_sats num_bc_tl num_il_tl num_op_rev_tl \
896093 5.0 8.0 9.0 15.0 13.0
668508 3.0 2.0 5.0 2.0 5.0
406913 4.0 2.0 9.0 5.0 9.0
233509 5.0 4.0 4.0 23.0 8.0
425880 6.0 3.0 4.0 8.0 8.0
... ... ... ... ... ...
929582 9.0 2.0 2.0 2.0 11.0
248545 9.0 7.0 9.0 41.0 14.0
918781 3.0 3.0 5.0 8.0 3.0
251703 5.0 8.0 10.0 12.0 15.0
762846 3.0 4.0 4.0 1.0 10.0
num_rev_accts num_rev_tl_bal_gt_0 num_sats num_tl_120dpd_2m \
896093 16.0 5.0 19.0 0.0
668508 10.0 3.0 7.0 0.0
406913 16.0 4.0 10.0 0.0
233509 8.0 5.0 13.0 0.0
425880 11.0 6.0 10.0 0.0
... ... ... ... ...
929582 14.0 9.0 13.0 0.0
248545 19.0 9.0 17.0 0.0
918781 8.0 3.0 4.0 0.0
251703 19.0 5.0 19.0 0.0
762846 14.0 3.0 12.0 0.0
num_tl_30dpd num_tl_90g_dpd_24m num_tl_op_past_12m pct_tl_nvr_dlq \
896093 0.0 0.0 4.0 100.0
668508 0.0 0.0 2.0 81.2
406913 0.0 0.0 2.0 100.0
233509 0.0 0.0 5.0 96.7
425880 0.0 0.0 2.0 100.0
... ... ... ... ...
929582 0.0 0.0 1.0 100.0
248545 0.0 0.0 5.0 94.9
918781 0.0 0.0 3.0 66.7
251703 0.0 0.0 3.0 100.0
762846 0.0 0.0 2.0 100.0
percent_bc_gt_75 pub_rec_bankruptcies tax_liens tot_hi_cred_lim \
896093 66.7 0.0 0.0 217357.0
668508 0.0 0.0 0.0 116352.0
406913 100.0 2.0 0.0 32998.0
233509 75.0 0.0 0.0 106264.0
425880 33.3 0.0 0.0 131718.0
... ... ... ... ...
929582 100.0 1.0 0.0 199862.0
248545 28.6 0.0 0.0 42100.0
918781 33.3 0.0 1.0 173200.0
251703 25.0 0.0 0.0 284593.0
762846 0.0 0.0 0.0 176906.0
total_bal_ex_mort total_bc_limit total_il_high_credit_limit \
896093 87318.0 23000.0 93288.0
668508 19870.0 3500.0 13855.0
406913 25187.0 4500.0 21998.0
233509 84718.0 10200.0 81564.0
425880 93998.0 28200.0 9718.0
... ... ... ...
929582 22291.0 5000.0 16146.0
248545 10226.0 27500.0 0.0
918781 4653.0 10000.0 0.0
251703 51598.0 51200.0 42631.0
762846 23619.0 21000.0 22500.0
revol_bal_joint sec_app_fico_range_low sec_app_fico_range_high \
896093 NaN NaN NaN
668508 NaN NaN NaN
406913 NaN NaN NaN
233509 NaN NaN NaN
425880 50192.0 690.0 694.0
... ... ... ...
929582 NaN NaN NaN
248545 NaN NaN NaN
918781 NaN NaN NaN
251703 NaN NaN NaN
762846 NaN NaN NaN
sec_app_earliest_cr_line sec_app_inq_last_6mths sec_app_mort_acc \
896093 NaN NaN NaN
668508 NaN NaN NaN
406913 NaN NaN NaN
233509 NaN NaN NaN
425880 Nov-91 0.0 1.0
... ... ... ...
929582 NaN NaN NaN
248545 NaN NaN NaN
918781 NaN NaN NaN
251703 NaN NaN NaN
762846 NaN NaN NaN
sec_app_open_acc sec_app_revol_util sec_app_open_act_il \
896093 NaN NaN NaN
668508 NaN NaN NaN
406913 NaN NaN NaN
233509 NaN NaN NaN
425880 16.0 78.3 0.0
... ... ... ...
929582 NaN NaN NaN
248545 NaN NaN NaN
918781 NaN NaN NaN
251703 NaN NaN NaN
762846 NaN NaN NaN
sec_app_num_rev_accts sec_app_chargeoff_within_12_mths \
896093 NaN NaN
668508 NaN NaN
406913 NaN NaN
233509 NaN NaN
425880 18.0 0.0
... ... ...
929582 NaN NaN
248545 NaN NaN
918781 NaN NaN
251703 NaN NaN
762846 NaN NaN
sec_app_collections_12_mths_ex_med \
896093 NaN
668508 NaN
406913 NaN
233509 NaN
425880 0.0
... ...
929582 NaN
248545 NaN
918781 NaN
251703 NaN
762846 NaN
sec_app_mths_since_last_major_derog hardship_flag hardship_type \
896093 NaN N NaN
668508 NaN N NaN
406913 NaN N NaN
233509 NaN N NaN
425880 NaN N NaN
... ... ... ...
929582 NaN N NaN
248545 NaN N NaN
918781 NaN N NaN
251703 NaN N NaN
762846 NaN N NaN
hardship_reason hardship_status deferral_term hardship_amount \
896093 NaN NaN NaN NaN
668508 NaN NaN NaN NaN
406913 NaN NaN NaN NaN
233509 NaN NaN NaN NaN
425880 NaN NaN NaN NaN
... ... ... ... ...
929582 NaN NaN NaN NaN
248545 NaN NaN NaN NaN
918781 NaN NaN NaN NaN
251703 NaN NaN NaN NaN
762846 NaN NaN NaN NaN
hardship_start_date hardship_end_date payment_plan_start_date \
896093 NaN NaN NaN
668508 NaN NaN NaN
406913 NaN NaN NaN
233509 NaN NaN NaN
425880 NaN NaN NaN
... ... ... ...
929582 NaN NaN NaN
248545 NaN NaN NaN
918781 NaN NaN NaN
251703 NaN NaN NaN
762846 NaN NaN NaN
hardship_length hardship_dpd hardship_loan_status \
896093 NaN NaN NaN
668508 NaN NaN NaN
406913 NaN NaN NaN
233509 NaN NaN NaN
425880 NaN NaN NaN
... ... ... ...
929582 NaN NaN NaN
248545 NaN NaN NaN
918781 NaN NaN NaN
251703 NaN NaN NaN
762846 NaN NaN NaN
orig_projected_additional_accrued_interest \
896093 NaN
668508 NaN
406913 NaN
233509 NaN
425880 NaN
... ...
929582 NaN
248545 NaN
918781 NaN
251703 NaN
762846 NaN
hardship_payoff_balance_amount hardship_last_payment_amount \
896093 NaN NaN
668508 NaN NaN
406913 NaN NaN
233509 NaN NaN
425880 NaN NaN
... ... ...
929582 NaN NaN
248545 NaN NaN
918781 NaN NaN
251703 NaN NaN
762846 NaN NaN
disbursement_method debt_settlement_flag debt_settlement_flag_date \
896093 Cash N NaN
668508 Cash N NaN
406913 Cash N NaN
233509 Cash N NaN
425880 Cash N NaN
... ... ... ...
929582 Cash N NaN
248545 Cash N NaN
918781 Cash N NaN
251703 Cash N NaN
762846 Cash N NaN
settlement_status settlement_date settlement_amount \
896093 NaN NaN NaN
668508 NaN NaN NaN
406913 NaN NaN NaN
233509 NaN NaN NaN
425880 NaN NaN NaN
... ... ... ...
929582 NaN NaN NaN
248545 NaN NaN NaN
918781 NaN NaN NaN
251703 NaN NaN NaN
762846 NaN NaN NaN
settlement_percentage settlement_term emp_length_int term_int \
896093 NaN NaN 10.0 36.0
668508 NaN NaN 6.0 36.0
406913 NaN NaN 10.0 36.0
233509 NaN NaN 8.0 36.0
425880 NaN NaN 0.0 60.0
... ... ... ... ...
929582 NaN NaN 10.0 60.0
248545 NaN NaN 0.0 36.0
918781 NaN NaN 0.0 36.0
251703 NaN NaN 5.0 36.0
762846 NaN NaN 0.0 36.0
earliest_cr_line_date mth_earliest_cr_line_date
896093 2003-10-01 257.0
668508 2002-04-01 276.0
406913 2003-02-01 265.0
233509 2001-02-01 290.0
425880 1982-04-01 519.0
... ... ...
929582 2005-06-01 237.0
248545 1989-10-01 428.0
918781 2001-09-01 283.0
251703 1993-01-01 388.0
762846 2011-10-01 160.0
[262144 rows x 155 columns],
583729 1
833277 1
87186 1
185251 1
165960 1
..
752960 1
335602 1
608791 1
421739 1
314591 1
Name: good_bad, Length: 786431, dtype: int32,
896093 1
668508 1
406913 1
233509 1
425880 1
..
929582 1
248545 1
918781 1
251703 1
762846 1
Name: good_bad, Length: 262144, dtype: int32]
In [63]:
loan_data_inputs_train, loan_data_inputs_test, loan_data_target_train, loan_data_target_test = train_test_split(loan_data.drop('good_bad', axis = 1),loan_data['good_bad'])
In [64]:
loan_data_inputs_train.shape
Out[64]:
(786431, 155)
In [65]:
loan_data_inputs_test.shape
Out[65]:
(262144, 155)
In [66]:
loan_data_target_train.shape
Out[66]:
(786431,)
In [67]:
loan_data_target_test.shape
Out[67]:
(262144,)
In [68]:
loan_data_inputs_train, loan_data_inputs_test, loan_data_target_train, loan_data_target_test = train_test_split(loan_data.drop('good_bad', axis = 1),loan_data['good_bad'], test_size = 0.2, random_state = 42)
In [69]:
loan_data_inputs_train.shape
Out[69]:
(838860, 155)
In [70]:
loan_data_inputs_test.shape
Out[70]:
(209715, 155)
In [71]:
loan_data_target_train.shape
Out[71]:
(838860,)
In [72]:
loan_data_target_test.shape
Out[72]:
(209715,)
In [74]:
df_inputs_prep = loan_data_inputs_train
df_target_prep = loan_data_target_train
In [ ]:
In [ ]:
In [144]:
#df_inputs_prep = loan_data_inputs_train
#df_target_prep = loan_data_target_train
df_inputs_prep = loan_data_inputs_test
df_target_prep = loan_data_target_test
In [75]:
df_inputs_prep['grade'].unique()
Out[75]:
array(['A', 'D', 'B', 'C', 'E', 'G', 'F', nan], dtype=object)
In [76]:
df1= pd.concat([df_inputs_prep['grade'], df_target_prep], axis = 1)
df1.head()
Out[76]:
| grade | good_bad | |
|---|---|---|
| 408561 | A | 1 |
| 70143 | D | 1 |
| 708782 | B | 1 |
| 572694 | C | 0 |
| 774181 | C | 1 |
In [77]:
df1.groupby(df1.columns.values[0], as_index = False)[df1.columns.values[1]].count()
Out[77]:
| grade | good_bad | |
|---|---|---|
| 0 | A | 168583 |
| 1 | B | 240772 |
| 2 | C | 244033 |
| 3 | D | 114624 |
| 4 | E | 51157 |
| 5 | F | 14782 |
| 6 | G | 4899 |
In [78]:
df1.groupby(df1.columns.values[0], as_index = False)[df1.columns.values[1]].mean()
Out[78]:
| grade | good_bad | |
|---|---|---|
| 0 | A | 0.965341 |
| 1 | B | 0.918425 |
| 2 | C | 0.863736 |
| 3 | D | 0.801193 |
| 4 | E | 0.718084 |
| 5 | F | 0.637126 |
| 6 | G | 0.612778 |
In [79]:
df1 = pd.concat([df1.groupby(df1.columns.values[0], as_index = False)[df1.columns.values[1]].count(),
df1.groupby(df1.columns.values[0], as_index = False)[df1.columns.values[1]].mean()], axis = 1)
In [80]:
df1
Out[80]:
| grade | good_bad | grade | good_bad | |
|---|---|---|---|---|
| 0 | A | 168583 | A | 0.965341 |
| 1 | B | 240772 | B | 0.918425 |
| 2 | C | 244033 | C | 0.863736 |
| 3 | D | 114624 | D | 0.801193 |
| 4 | E | 51157 | E | 0.718084 |
| 5 | F | 14782 | F | 0.637126 |
| 6 | G | 4899 | G | 0.612778 |
In [81]:
df1 = df1.iloc[: , [0, 1, 3]]
In [82]:
df1
Out[82]:
| grade | good_bad | good_bad | |
|---|---|---|---|
| 0 | A | 168583 | 0.965341 |
| 1 | B | 240772 | 0.918425 |
| 2 | C | 244033 | 0.863736 |
| 3 | D | 114624 | 0.801193 |
| 4 | E | 51157 | 0.718084 |
| 5 | F | 14782 | 0.637126 |
| 6 | G | 4899 | 0.612778 |
In [83]:
df1.columns = [df1.columns.values[0], 'n_obs', 'prop_good']
In [84]:
df1
Out[84]:
| grade | n_obs | prop_good | |
|---|---|---|---|
| 0 | A | 168583 | 0.965341 |
| 1 | B | 240772 | 0.918425 |
| 2 | C | 244033 | 0.863736 |
| 3 | D | 114624 | 0.801193 |
| 4 | E | 51157 | 0.718084 |
| 5 | F | 14782 | 0.637126 |
| 6 | G | 4899 | 0.612778 |
In [85]:
df1['prop_n_obs'] = df1['n_obs'] / df1['n_obs'].sum()
df1
Out[85]:
| grade | n_obs | prop_good | prop_n_obs | |
|---|---|---|---|---|
| 0 | A | 168583 | 0.965341 | 0.200969 |
| 1 | B | 240772 | 0.918425 | 0.287026 |
| 2 | C | 244033 | 0.863736 | 0.290914 |
| 3 | D | 114624 | 0.801193 | 0.136644 |
| 4 | E | 51157 | 0.718084 | 0.060985 |
| 5 | F | 14782 | 0.637126 | 0.017622 |
| 6 | G | 4899 | 0.612778 | 0.005840 |
In [86]:
df1['good_number'] = df1['n_obs'] * df1['prop_good']
df1['bad_number'] = (1 - df1['prop_good']) * df1['n_obs']
df1
Out[86]:
| grade | n_obs | prop_good | prop_n_obs | good_number | bad_number | |
|---|---|---|---|---|---|---|
| 0 | A | 168583 | 0.965341 | 0.200969 | 162740.0 | 5843.0 |
| 1 | B | 240772 | 0.918425 | 0.287026 | 221131.0 | 19641.0 |
| 2 | C | 244033 | 0.863736 | 0.290914 | 210780.0 | 33253.0 |
| 3 | D | 114624 | 0.801193 | 0.136644 | 91836.0 | 22788.0 |
| 4 | E | 51157 | 0.718084 | 0.060985 | 36735.0 | 14422.0 |
| 5 | F | 14782 | 0.637126 | 0.017622 | 9418.0 | 5364.0 |
| 6 | G | 4899 | 0.612778 | 0.005840 | 3002.0 | 1897.0 |
In [87]:
df1['prop_good_bor'] = df1['good_number'] / df1['good_number'].sum()
df1['prop_bad_bor'] = df1['bad_number'] / df1['bad_number'].sum()
df1
Out[87]:
| grade | n_obs | prop_good | prop_n_obs | good_number | bad_number | prop_good_bor | prop_bad_bor | |
|---|---|---|---|---|---|---|---|---|
| 0 | A | 168583 | 0.965341 | 0.200969 | 162740.0 | 5843.0 | 0.221222 | 0.056614 |
| 1 | B | 240772 | 0.918425 | 0.287026 | 221131.0 | 19641.0 | 0.300596 | 0.190305 |
| 2 | C | 244033 | 0.863736 | 0.290914 | 210780.0 | 33253.0 | 0.286525 | 0.322194 |
| 3 | D | 114624 | 0.801193 | 0.136644 | 91836.0 | 22788.0 | 0.124838 | 0.220797 |
| 4 | E | 51157 | 0.718084 | 0.060985 | 36735.0 | 14422.0 | 0.049936 | 0.139737 |
| 5 | F | 14782 | 0.637126 | 0.017622 | 9418.0 | 5364.0 | 0.012802 | 0.051973 |
| 6 | G | 4899 | 0.612778 | 0.005840 | 3002.0 | 1897.0 | 0.004081 | 0.018380 |
In [88]:
df1['WoE'] = np.log(df1['prop_good_bor']/df1['prop_bad_bor'])
df1
Out[88]:
| grade | n_obs | prop_good | prop_n_obs | good_number | bad_number | prop_good_bor | prop_bad_bor | WoE | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | A | 168583 | 0.965341 | 0.200969 | 162740.0 | 5843.0 | 0.221222 | 0.056614 | 1.362912 |
| 1 | B | 240772 | 0.918425 | 0.287026 | 221131.0 | 19641.0 | 0.300596 | 0.190305 | 0.457139 |
| 2 | C | 244033 | 0.863736 | 0.290914 | 210780.0 | 33253.0 | 0.286525 | 0.322194 | -0.117327 |
| 3 | D | 114624 | 0.801193 | 0.136644 | 91836.0 | 22788.0 | 0.124838 | 0.220797 | -0.570227 |
| 4 | E | 51157 | 0.718084 | 0.060985 | 36735.0 | 14422.0 | 0.049936 | 0.139737 | -1.029022 |
| 5 | F | 14782 | 0.637126 | 0.017622 | 9418.0 | 5364.0 | 0.012802 | 0.051973 | -1.401084 |
| 6 | G | 4899 | 0.612778 | 0.005840 | 3002.0 | 1897.0 | 0.004081 | 0.018380 | -1.504992 |
In [89]:
df1 = df1.sort_values(['WoE'])
df1 = df1.reset_index(drop = True)
df1
Out[89]:
| grade | n_obs | prop_good | prop_n_obs | good_number | bad_number | prop_good_bor | prop_bad_bor | WoE | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | G | 4899 | 0.612778 | 0.005840 | 3002.0 | 1897.0 | 0.004081 | 0.018380 | -1.504992 |
| 1 | F | 14782 | 0.637126 | 0.017622 | 9418.0 | 5364.0 | 0.012802 | 0.051973 | -1.401084 |
| 2 | E | 51157 | 0.718084 | 0.060985 | 36735.0 | 14422.0 | 0.049936 | 0.139737 | -1.029022 |
| 3 | D | 114624 | 0.801193 | 0.136644 | 91836.0 | 22788.0 | 0.124838 | 0.220797 | -0.570227 |
| 4 | C | 244033 | 0.863736 | 0.290914 | 210780.0 | 33253.0 | 0.286525 | 0.322194 | -0.117327 |
| 5 | B | 240772 | 0.918425 | 0.287026 | 221131.0 | 19641.0 | 0.300596 | 0.190305 | 0.457139 |
| 6 | A | 168583 | 0.965341 | 0.200969 | 162740.0 | 5843.0 | 0.221222 | 0.056614 | 1.362912 |
In [90]:
df1['IV'] = df1['prop_good_bor'] - df1['prop_bad_bor'] * df1['WoE']
df1['IV'] = df1['IV'].sum()
df1
Out[90]:
| grade | n_obs | prop_good | prop_n_obs | good_number | bad_number | prop_good_bor | prop_bad_bor | WoE | IV | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | G | 4899 | 0.612778 | 0.005840 | 3002.0 | 1897.0 | 0.004081 | 0.018380 | -1.504992 | 1.243824 |
| 1 | F | 14782 | 0.637126 | 0.017622 | 9418.0 | 5364.0 | 0.012802 | 0.051973 | -1.401084 | 1.243824 |
| 2 | E | 51157 | 0.718084 | 0.060985 | 36735.0 | 14422.0 | 0.049936 | 0.139737 | -1.029022 | 1.243824 |
| 3 | D | 114624 | 0.801193 | 0.136644 | 91836.0 | 22788.0 | 0.124838 | 0.220797 | -0.570227 | 1.243824 |
| 4 | C | 244033 | 0.863736 | 0.290914 | 210780.0 | 33253.0 | 0.286525 | 0.322194 | -0.117327 | 1.243824 |
| 5 | B | 240772 | 0.918425 | 0.287026 | 221131.0 | 19641.0 | 0.300596 | 0.190305 | 0.457139 | 1.243824 |
| 6 | A | 168583 | 0.965341 | 0.200969 | 162740.0 | 5843.0 | 0.221222 | 0.056614 | 1.362912 | 1.243824 |
In [91]:
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
def plot_by_woe(df_WoE, rotation_of_axis_labels=0):
x = np.array(df_WoE.iloc[:, 0].apply(str))
y = df_WoE['WoE']
plt.figure(figsize=(18, 6))
plt.plot(x, y, marker='o', linestyle='--', color='k')
plt.xlabel(df_WoE.columns[0])
plt.ylabel('Weight of evidence')
plt.title(f'Weight of evidence by {df_WoE.columns[0]}')
plt.xticks(rotation=rotation_of_axis_labels)
plt.show()
In [ ]:
In [92]:
plot_by_woe(df1, rotation_of_axis_labels=45)
In [93]:
df_inputs_prep['grade:A'] = np.where(df_inputs_prep['grade'] == 'A', 1, 0)
df_inputs_prep['grade:B'] = np.where(df_inputs_prep['grade'] == 'B', 1, 0)
df_inputs_prep['grade:C'] = np.where(df_inputs_prep['grade'] == 'C', 1, 0)
df_inputs_prep['grade:D'] = np.where(df_inputs_prep['grade'] == 'D', 1, 0)
df_inputs_prep['grade:E'] = np.where(df_inputs_prep['grade'] == 'E', 1, 0)
df_inputs_prep['grade:F'] = np.where(df_inputs_prep['grade'] == 'F', 1, 0)
df_inputs_prep['grade:G'] = np.where(df_inputs_prep['grade'] == 'G', 1, 0)
In [94]:
def woe_discrete(df, discrete_variable_name, good_bad_variable_df):
df = pd.concat([df[discrete_variable_name], good_bad_variable_df], axis=1)
df = pd.concat([
df.groupby(df.columns.values[0], as_index=False)[df.columns.values[1]].count(),
df.groupby(df.columns.values[0], as_index=False)[df.columns.values[1]].mean()
], axis=1)
df = df.iloc[:, [0, 1, 3]]
df.columns = [df.columns.values[0], 'n_obs', 'prop_good']
df['prop_n_obs'] = df['n_obs'] / df['n_obs'].sum()
df['good_number'] = df['n_obs'] * df['prop_good']
df['bad_number'] = (1 - df['prop_good']) * df['n_obs']
df['prop_good_bor'] = df['good_number'] / df['good_number'].sum()
df['prop_bad_bor'] = df['bad_number'] / df['bad_number'].sum()
df['WoE'] = np.log(df['prop_good_bor'] / df['prop_bad_bor'])
df = df.sort_values(['WoE'])
df = df.reset_index(drop=True)
df['IV'] = (df['prop_good_bor'] - df['prop_bad_bor']) * df['WoE']
df['IV'] = df['IV'].sum()
return df
In [95]:
df_temp = woe_discrete(df_inputs_prep,'home_ownership', df_target_prep)
df_temp
Out[95]:
| home_ownership | n_obs | prop_good | prop_n_obs | good_number | bad_number | prop_good_bor | prop_bad_bor | WoE | IV | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | RENT | 331269 | 0.853946 | 0.394909 | 282886.0 | 48383.0 | 0.384543 | 0.468791 | -0.198102 | inf |
| 1 | OWN | 96988 | 0.876098 | 0.115620 | 84971.0 | 12017.0 | 0.115506 | 0.116435 | -0.008010 | inf |
| 2 | MORTGAGE | 410467 | 0.895709 | 0.489321 | 367659.0 | 42808.0 | 0.499780 | 0.414774 | 0.186434 | inf |
| 3 | ANY | 123 | 1.000000 | 0.000147 | 123.0 | 0.0 | 0.000167 | 0.000000 | inf | inf |
| 4 | NONE | 3 | 1.000000 | 0.000004 | 3.0 | 0.0 | 0.000004 | 0.000000 | inf | inf |
In [96]:
plot_by_woe(df_temp)
In [97]:
import pandas as pd
# Assuming df_inputs_prep is already loaded and contains the 'home_ownership' column
# Create dummy variables for 'home_ownership'
df_inputs_prep['home_ownership:MORTGAGE'] = df_inputs_prep['home_ownership'].apply(lambda x: 1 if x in ['MORTGAGE'] else 0)
df_inputs_prep['home_ownership:OWN'] = df_inputs_prep['home_ownership'].apply(lambda x: 1 if x == 'OWN' else 0)
df_inputs_prep['home_ownership:RENT'] = df_inputs_prep['home_ownership'].apply(lambda x: 1 if x == 'RENT' else 0)
In [98]:
df_inputs_prep['addr_state'].unique()
Out[98]:
array(['NY', 'IL', 'PA', 'TX', 'MN', 'NJ', 'AL', 'CA', 'OK', 'AZ', 'NH',
'CT', 'TN', 'MD', 'GA', 'AR', 'MA', 'KY', 'OH', 'NC', 'FL', 'LA',
'CO', 'NM', 'MI', 'ME', 'KS', 'MO', 'VA', 'IN', 'WA', 'SC', 'DE',
'AK', 'NV', 'WI', 'OR', 'HI', 'WY', 'SD', 'WV', 'RI', 'ND', 'UT',
'NE', 'MS', 'MT', 'DC', 'ID', 'VT', nan], dtype=object)
In [99]:
df_temp = woe_discrete(df_inputs_prep,'addr_state', df_target_prep)
df_temp
Out[99]:
| addr_state | n_obs | prop_good | prop_n_obs | good_number | bad_number | prop_good_bor | prop_bad_bor | WoE | IV | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | AL | 10260 | 0.847856 | 0.012231 | 8699.0 | 1561.0 | 0.011825 | 0.015125 | -0.246116 | 0.017212 |
| 1 | AR | 6297 | 0.851675 | 0.007507 | 5363.0 | 934.0 | 0.007290 | 0.009050 | -0.216195 | 0.017212 |
| 2 | OK | 7767 | 0.853483 | 0.009259 | 6629.0 | 1138.0 | 0.009011 | 0.011026 | -0.201816 | 0.017212 |
| 3 | MS | 5304 | 0.855769 | 0.006323 | 4539.0 | 765.0 | 0.006170 | 0.007412 | -0.183411 | 0.017212 |
| 4 | SD | 1715 | 0.855977 | 0.002044 | 1468.0 | 247.0 | 0.001996 | 0.002393 | -0.181729 | 0.017212 |
| 5 | LA | 9600 | 0.857500 | 0.011444 | 8232.0 | 1368.0 | 0.011190 | 0.013255 | -0.169318 | 0.017212 |
| 6 | NM | 4504 | 0.860124 | 0.005369 | 3874.0 | 630.0 | 0.005266 | 0.006104 | -0.147674 | 0.017212 |
| 7 | NV | 12129 | 0.860170 | 0.014459 | 10433.0 | 1696.0 | 0.014182 | 0.016433 | -0.147296 | 0.017212 |
| 8 | HI | 3793 | 0.862905 | 0.004522 | 3273.0 | 520.0 | 0.004449 | 0.005038 | -0.124364 | 0.017212 |
| 9 | NY | 68522 | 0.865065 | 0.081686 | 59276.0 | 9246.0 | 0.080577 | 0.089586 | -0.105984 | 0.017212 |
| 10 | FL | 60113 | 0.867965 | 0.071661 | 52176.0 | 7937.0 | 0.070926 | 0.076903 | -0.080910 | 0.017212 |
| 11 | IN | 14588 | 0.868179 | 0.017390 | 12665.0 | 1923.0 | 0.017216 | 0.018632 | -0.079041 | 0.017212 |
| 12 | NE | 3407 | 0.868800 | 0.004062 | 2960.0 | 447.0 | 0.004024 | 0.004331 | -0.073611 | 0.017212 |
| 13 | NC | 23527 | 0.870787 | 0.028047 | 20487.0 | 3040.0 | 0.027849 | 0.029455 | -0.056064 | 0.017212 |
| 14 | KY | 8300 | 0.871325 | 0.009894 | 7232.0 | 1068.0 | 0.009831 | 0.010348 | -0.051269 | 0.017212 |
| 15 | MO | 13482 | 0.871458 | 0.016072 | 11749.0 | 1733.0 | 0.015971 | 0.016791 | -0.050083 | 0.017212 |
| 16 | MD | 20105 | 0.872171 | 0.023967 | 17535.0 | 2570.0 | 0.023836 | 0.024901 | -0.043704 | 0.017212 |
| 17 | OH | 28452 | 0.872944 | 0.033918 | 24837.0 | 3615.0 | 0.033762 | 0.035026 | -0.036755 | 0.017212 |
| 18 | CA | 112778 | 0.873238 | 0.134444 | 98482.0 | 14296.0 | 0.133872 | 0.138516 | -0.034103 | 0.017212 |
| 19 | PA | 28506 | 0.873465 | 0.033982 | 24899.0 | 3607.0 | 0.033847 | 0.034949 | -0.032046 | 0.017212 |
| 20 | NJ | 30419 | 0.874158 | 0.036263 | 26591.0 | 3828.0 | 0.036147 | 0.037090 | -0.025767 | 0.017212 |
| 21 | AZ | 19908 | 0.874523 | 0.023732 | 17410.0 | 2498.0 | 0.023666 | 0.024204 | -0.022443 | 0.017212 |
| 22 | AK | 1922 | 0.874610 | 0.002291 | 1681.0 | 241.0 | 0.002285 | 0.002335 | -0.021650 | 0.017212 |
| 23 | MI | 22346 | 0.874787 | 0.026639 | 19548.0 | 2798.0 | 0.026573 | 0.027110 | -0.020029 | 0.017212 |
| 24 | TN | 13808 | 0.875145 | 0.016461 | 12084.0 | 1724.0 | 0.016426 | 0.016704 | -0.016762 | 0.017212 |
| 25 | MN | 14866 | 0.875555 | 0.017722 | 13016.0 | 1850.0 | 0.017693 | 0.017925 | -0.013003 | 0.017212 |
| 26 | TX | 69541 | 0.877137 | 0.082900 | 60997.0 | 8544.0 | 0.082917 | 0.082784 | 0.001598 | 0.017212 |
| 27 | VA | 23292 | 0.877941 | 0.027767 | 20449.0 | 2843.0 | 0.027797 | 0.027546 | 0.009077 | 0.017212 |
| 28 | DE | 2432 | 0.878289 | 0.002899 | 2136.0 | 296.0 | 0.002904 | 0.002868 | 0.012334 | 0.017212 |
| 29 | MA | 19039 | 0.882609 | 0.022697 | 16804.0 | 2235.0 | 0.022843 | 0.021655 | 0.053379 | 0.017212 |
| 30 | UT | 5349 | 0.883343 | 0.006377 | 4725.0 | 624.0 | 0.006423 | 0.006046 | 0.060475 | 0.017212 |
| 31 | WI | 11204 | 0.885666 | 0.013356 | 9923.0 | 1281.0 | 0.013489 | 0.012412 | 0.083217 | 0.017212 |
| 32 | GA | 27830 | 0.888969 | 0.033176 | 24740.0 | 3090.0 | 0.033630 | 0.029940 | 0.116253 | 0.017212 |
| 33 | WY | 1745 | 0.889971 | 0.002080 | 1553.0 | 192.0 | 0.002111 | 0.001860 | 0.126451 | 0.017212 |
| 34 | RI | 3646 | 0.890016 | 0.004346 | 3245.0 | 401.0 | 0.004411 | 0.003885 | 0.126912 | 0.017212 |
| 35 | IL | 34090 | 0.893018 | 0.040639 | 30443.0 | 3647.0 | 0.041383 | 0.035336 | 0.157954 | 0.017212 |
| 36 | ND | 1616 | 0.895421 | 0.001926 | 1447.0 | 169.0 | 0.001967 | 0.001637 | 0.183352 | 0.017212 |
| 37 | MT | 2334 | 0.897172 | 0.002782 | 2094.0 | 240.0 | 0.002846 | 0.002325 | 0.202195 | 0.017212 |
| 38 | KS | 7081 | 0.899590 | 0.008441 | 6370.0 | 711.0 | 0.008659 | 0.006889 | 0.228685 | 0.017212 |
| 39 | CT | 13141 | 0.900997 | 0.015665 | 11840.0 | 1301.0 | 0.016095 | 0.012606 | 0.244353 | 0.017212 |
| 40 | CO | 17887 | 0.904456 | 0.021323 | 16178.0 | 1709.0 | 0.021992 | 0.016559 | 0.283747 | 0.017212 |
| 41 | WV | 3102 | 0.904900 | 0.003698 | 2807.0 | 295.0 | 0.003816 | 0.002858 | 0.288899 | 0.017212 |
| 42 | WA | 17214 | 0.905135 | 0.020521 | 15581.0 | 1633.0 | 0.021180 | 0.015822 | 0.291636 | 0.017212 |
| 43 | SC | 10449 | 0.907264 | 0.012456 | 9480.0 | 969.0 | 0.012887 | 0.009389 | 0.316678 | 0.017212 |
| 44 | DC | 1871 | 0.908071 | 0.002230 | 1699.0 | 172.0 | 0.002310 | 0.001667 | 0.326303 | 0.017212 |
| 45 | NH | 4154 | 0.912614 | 0.004952 | 3791.0 | 363.0 | 0.005153 | 0.003517 | 0.381985 | 0.017212 |
| 46 | OR | 9777 | 0.915925 | 0.011655 | 8955.0 | 822.0 | 0.012173 | 0.007964 | 0.424230 | 0.017212 |
| 47 | ID | 1719 | 0.922048 | 0.002049 | 1585.0 | 134.0 | 0.002155 | 0.001298 | 0.506503 | 0.017212 |
| 48 | VT | 1834 | 0.930752 | 0.002186 | 1707.0 | 127.0 | 0.002320 | 0.001231 | 0.634308 | 0.017212 |
| 49 | ME | 2085 | 0.937650 | 0.002486 | 1955.0 | 130.0 | 0.002658 | 0.001260 | 0.746614 | 0.017212 |
In [100]:
plot_by_woe(df_temp)
In [101]:
plot_by_woe(df_temp)
In [102]:
df_inputs_prep['addr_state:AL_AR'] = df_inputs_prep['addr_state'].apply(lambda x: 1 if x in ['AL', 'AR'] else 0)
df_inputs_prep['addr_state:OK_MS_SD_LA_NM_NV_HI'] = df_inputs_prep['addr_state'].apply(lambda x: 1 if x in ['OK','MS','SD','LA','NM','NV','HI'] else 0)
df_inputs_prep['addr_state:NY_FL_IN_NE_NC_KY_MO_MD_OH_CA_PA_NJ_AZ_AK_MI_TN_MN_TX_VA_DE'] = df_inputs_prep['addr_state'].apply(lambda x: 1 if x in ['NY','FL','IN','NE','NC','KY','MO','MD','OH','CA','PA','NJ','AZ','MI','TN','MN','TX','VA','DE'] else 0)
df_inputs_prep['addr_state:MA_UT_QI_GA_WY_RI_IL_ND_MT_KS_CT_CO_WV_WA_SC_DC_NH_OR_VT_ME'] = df_inputs_prep['addr_state'].apply(lambda x: 1 if x in ['MA','UT','WI','GA','WY','RI','IL','ND','MT','KS','CT','CO','WV','SC','DC','NH','OR','ID','VT','ME'] else 0)
In [103]:
df_inputs_prep['verification_status:Verified'] = np.where(df_inputs_prep['verification_status'] == 'Verified', 1, 0)
df_inputs_prep['verification_status:Source_Verified'] = np.where(df_inputs_prep['verification_status'] == 'Source Verified', 1, 0)
df_inputs_prep['verification_status:Not_Verified'] = np.where(df_inputs_prep['verification_status'] == 'Not Verified', 1, 0)
In [104]:
df_temp = woe_discrete(df_inputs_prep,'purpose', df_target_prep)
df_temp
Out[104]:
| purpose | n_obs | prop_good | prop_n_obs | good_number | bad_number | prop_good_bor | prop_bad_bor | WoE | IV | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | renewable_energy | 483 | 0.815735 | 0.000576 | 394.0 | 89.0 | 0.000536 | 0.000862 | -0.476283 | inf |
| 1 | small_business | 7800 | 0.819103 | 0.009298 | 6389.0 | 1411.0 | 0.008685 | 0.013671 | -0.453718 | inf |
| 2 | moving | 5745 | 0.848042 | 0.006849 | 4872.0 | 873.0 | 0.006623 | 0.008459 | -0.244673 | inf |
| 3 | wedding | 7 | 0.857143 | 0.000008 | 6.0 | 1.0 | 0.000008 | 0.000010 | -0.172238 | inf |
| 4 | debt_consolidation | 475865 | 0.866462 | 0.567283 | 412319.0 | 63546.0 | 0.560489 | 0.615708 | -0.093964 | inf |
| 5 | medical | 10211 | 0.872393 | 0.012173 | 8908.0 | 1303.0 | 0.012109 | 0.012625 | -0.041717 | inf |
| 6 | other | 49872 | 0.878629 | 0.059453 | 43819.0 | 6053.0 | 0.059566 | 0.058649 | 0.015516 | inf |
| 7 | house | 4551 | 0.880246 | 0.005425 | 4006.0 | 545.0 | 0.005446 | 0.005281 | 0.030766 | inf |
| 8 | vacation | 5958 | 0.881504 | 0.007103 | 5252.0 | 706.0 | 0.007139 | 0.006841 | 0.042752 | inf |
| 9 | major_purchase | 18293 | 0.886623 | 0.021807 | 16219.0 | 2074.0 | 0.022047 | 0.020095 | 0.092707 | inf |
| 10 | home_improvement | 57844 | 0.893731 | 0.068956 | 51697.0 | 6147.0 | 0.070275 | 0.059559 | 0.165438 | inf |
| 11 | credit_card | 193763 | 0.898293 | 0.230986 | 174056.0 | 19707.0 | 0.236604 | 0.190945 | 0.214406 | inf |
| 12 | car | 8457 | 0.910961 | 0.010082 | 7704.0 | 753.0 | 0.010472 | 0.007296 | 0.361433 | inf |
| 13 | educational | 1 | 1.000000 | 0.000001 | 1.0 | 0.0 | 0.000001 | 0.000000 | inf | inf |
In [105]:
plot_by_woe(df_temp)
In [106]:
df_inputs_prep['purpose:small_business_moving_debt_consolidation'] = df_inputs_prep['purpose'].apply(lambda x: 1 if x in ['small_business', 'moving', 'debt_consolidation'] else 0)
df_inputs_prep['purpose:medical_house_other_renewable_energy_major_purchase'] = df_inputs_prep['purpose'].apply(lambda x: 1 if x in ['medical', 'house', 'other', 'renewable_energy', 'major_purchase'] else 0)
df_inputs_prep['purpose:home_improvement_vacation_car_credit_card'] = df_inputs_prep['purpose'].apply(lambda x: 1 if x in ['home_improvement', 'vacation', 'car', 'credit_card'] else 0)
In [107]:
df_temp = woe_discrete(df_inputs_prep,'initial_list_status', df_target_prep)
df_temp
Out[107]:
| initial_list_status | n_obs | prop_good | prop_n_obs | good_number | bad_number | prop_good_bor | prop_bad_bor | WoE | IV | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | f | 217138 | 0.838103 | 0.258852 | 181984.0 | 35154.0 | 0.247381 | 0.340613 | -0.319817 | 0.042147 |
| 1 | w | 621712 | 0.890538 | 0.741148 | 553658.0 | 68054.0 | 0.752619 | 0.659387 | 0.132248 | 0.042147 |
In [108]:
plot_by_woe(df_temp)
In [109]:
df_inputs_prep['initial_list_status:w'] = np.where(df_inputs_prep['initial_list_status'] == 'w', 1, 0)
df_inputs_prep['initial_list_status:f'] = np.where(df_inputs_prep['initial_list_status'] == 'f', 1, 0)
In [110]:
def woe_ordered_continuous(df, discrete_variable_name, good_bad_variable_df):
df = pd.concat([df[discrete_variable_name], good_bad_variable_df], axis=1)
df = pd.concat([
df.groupby(df.columns.values[0], as_index=False)[df.columns.values[1]].count(),
df.groupby(df.columns.values[0], as_index=False)[df.columns.values[1]].mean()
], axis=1)
df = df.iloc[:, [0, 1, 3]]
df.columns = [df.columns.values[0], 'n_obs', 'prop_good']
df['prop_n_obs'] = df['n_obs'] / df['n_obs'].sum()
df['good_number'] = df['n_obs'] * df['prop_good']
df['bad_number'] = (1 - df['prop_good']) * df['n_obs']
df['prop_good_bor'] = df['good_number'] / df['good_number'].sum()
df['prop_bad_bor'] = df['bad_number'] / df['bad_number'].sum()
df['WoE'] = np.log(df['prop_good_bor'] / df['prop_bad_bor'])
#df = df.sort_values(['WoE'])
#df = df.reset_index(drop=True)
df['IV'] = (df['prop_good_bor'] - df['prop_bad_bor']) * df['WoE']
df['IV'] = df['IV'].sum()
return df
In [111]:
df_inputs_prep['term_int'].unique()
Out[111]:
array([36., 60., nan])
In [112]:
df_temp = woe_ordered_continuous(df_inputs_prep, 'term_int', df_target_prep)
df_temp
Out[112]:
| term_int | n_obs | prop_good | prop_n_obs | good_number | bad_number | prop_good_bor | prop_bad_bor | WoE | IV | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 36.0 | 589160 | 0.894241 | 0.702342 | 526851.0 | 62309.0 | 0.716179 | 0.603723 | 0.170815 | 0.056743 |
| 1 | 60.0 | 249690 | 0.836201 | 0.297658 | 208791.0 | 40899.0 | 0.283821 | 0.396277 | -0.333769 | 0.056743 |
In [113]:
plot_by_woe(df_temp)
In [114]:
df_inputs_prep['term:36'] = np.where((df_inputs_prep['term_int'] == 36),1,0)
df_inputs_prep['term:60'] = np.where((df_inputs_prep['term_int'] == 60),1,0)
In [115]:
df_inputs_prep['emp_length_int'].unique()
Out[115]:
array([ 6., 10., 7., 3., 1., 8., 0., 2., 5., 9., 4.])
In [116]:
df_temp = woe_ordered_continuous(df_inputs_prep, 'emp_length_int', df_target_prep)
df_temp
Out[116]:
| emp_length_int | n_obs | prop_good | prop_n_obs | good_number | bad_number | prop_good_bor | prop_bad_bor | WoE | IV | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | 126838 | 0.864157 | 0.151203 | 109608.0 | 17230.0 | 0.148994 | 0.166944 | -0.113752 | 0.006372 |
| 1 | 1.0 | 55258 | 0.868490 | 0.065873 | 47991.0 | 7267.0 | 0.065236 | 0.070411 | -0.076341 | 0.006372 |
| 2 | 2.0 | 75835 | 0.875427 | 0.090402 | 66388.0 | 9447.0 | 0.090244 | 0.091534 | -0.014192 | 0.006372 |
| 3 | 3.0 | 67472 | 0.876660 | 0.080433 | 59150.0 | 8322.0 | 0.080405 | 0.080633 | -0.002837 | 0.006372 |
| 4 | 4.0 | 50637 | 0.877896 | 0.060364 | 44454.0 | 6183.0 | 0.060428 | 0.059908 | 0.008641 | 0.006372 |
| 5 | 5.0 | 50500 | 0.878376 | 0.060201 | 44358.0 | 6142.0 | 0.060298 | 0.059511 | 0.013132 | 0.006372 |
| 6 | 6.0 | 35149 | 0.882415 | 0.041901 | 31016.0 | 4133.0 | 0.042161 | 0.040045 | 0.051489 | 0.006372 |
| 7 | 7.0 | 32678 | 0.872422 | 0.038955 | 28509.0 | 4169.0 | 0.038753 | 0.040394 | -0.041467 | 0.006372 |
| 8 | 8.0 | 34550 | 0.863213 | 0.041187 | 29824.0 | 4726.0 | 0.040541 | 0.045791 | -0.121777 | 0.006372 |
| 9 | 9.0 | 30038 | 0.869399 | 0.035808 | 26115.0 | 3923.0 | 0.035499 | 0.038011 | -0.068358 | 0.006372 |
| 10 | 10.0 | 279905 | 0.886869 | 0.333673 | 248239.0 | 31666.0 | 0.337441 | 0.306817 | 0.095138 | 0.006372 |
In [117]:
plot_by_woe(df_temp)
In [118]:
df_inputs_prep['emp_length:0'] = np.where(df_inputs_prep['emp_length_int'].isin([0]),1,0)
df_inputs_prep['emp_length:1'] = np.where(df_inputs_prep['emp_length_int'].isin([1]),1,0)
df_inputs_prep['emp_length:2'] = np.where(df_inputs_prep['emp_length_int'].isin([2]),1,0)
df_inputs_prep['emp_length:3'] = np.where(df_inputs_prep['emp_length_int'].isin([3]),1,0)
df_inputs_prep['emp_length:4'] = np.where(df_inputs_prep['emp_length_int'].isin([4]),1,0)
df_inputs_prep['emp_length:5-6'] = np.where(df_inputs_prep['emp_length_int'].isin(range(5,7)),1,0)
df_inputs_prep['emp_length:7'] = np.where(df_inputs_prep['emp_length_int'].isin([7]),1,0)
df_inputs_prep['emp_length:8'] = np.where(df_inputs_prep['emp_length_int'].isin([8]),1,0)
df_inputs_prep['emp_length:9'] = np.where(df_inputs_prep['emp_length_int'].isin([9]),1,0)
df_inputs_prep['emp_length:10'] = np.where(df_inputs_prep['emp_length_int'].isin([10]),1,0)
In [119]:
df_inputs_prep['mth_earliest_cr_line_date'].unique()
Out[119]:
array([276., 367., 376., 220., 179., 264., 499., 394., 363., 369., 328.,
523., 326., 267., 243., 339., 332., 427., 275., 209., 531., 171.,
173., 234., 131., 252., 318., 319., 270., 475., 357., 233., 356.,
336., 295., 154., 282., 258., 632., 288., 201., 147., 296., 342.,
324., 437., 338., 283., 292., 351., 380., 218., 362., 226., 251.,
250., 169., 256., 247., 393., 254., 528., 260., 480., 195., 178.,
303., 343., 490., 680., 207., 574., 329., 352., 186., 337., 290.,
323., 301., 306., 398., 626., 273., 372., 166., 265., 246., 368.,
374., 281., 177., 277., 679., 215., 238., 465., 161., 360., 240.,
460., 146., 431., 381., 193., 197., 383., 271., 391., 223., 266.,
152., 156., 228., 466., 231., 289., 557., 308., 241., 213., 263.,
344., 413., 294., 211., 445., 214., 298., 373., 253., 217., 455.,
366., 236., 320., 382., 444., 384., 286., 297., 359., 299., 230.,
196., 249., 436., 269., 255., 349., 162., 130., 232., 280., 225.,
145., 621., 219., 124., 174., 316., 397., 331., 392., 153., 284.,
257., 379., 159., 245., 584., 203., 221., 314., 311., 239., 305.,
481., 561., 259., 469., 242., 304., 433., 602., 403., 188., 365.,
358., 353., 387., 167., 354., 244., 170., 317., 293., 334., 310.,
355., 456., 224., 350., 185., 459., 396., 409., 424., 410., 200.,
635., 371., 181., 205., 451., 272., 407., 194., 309., 402., 347.,
155., 202., 608., 222., 192., 448., 210., 370., 335., 416., 386.,
430., 473., 183., 287., 509., 158., 163., 435., 285., 307., 514.,
149., 408., 261., 446., 322., 429., 401., 341., 485., 235., 315.,
544., 411., 434., 596., 248., 495., 135., 227., 340., 452., 125.,
467., 148., 212., 133., 454., 175., 321., 464., 143., 348., 327.,
432., 423., 599., 492., 229., 330., 457., 237., 508., 312., 262.,
422., 313., 479., 291., 172., 300., 470., 484., 438., 418., 150.,
346., 461., 198., 443., 538., 520., 426., 419., 472., 333., 474.,
157., 462., 609., 389., 487., 388., 450., 666., 118., 268., 385.,
134., 525., 406., 279., 535., 477., 619., 622., 478., 453., 164.,
361., 610., 458., 516., 176., 556., 512., 513., 302., 549., 641.,
375., 187., 532., 325., 620., 605., 598., 204., 428., 377., 199.,
182., 414., 208., 378., 441., 618., 160., 405., 493., 425., 168.,
129., 141., 364., 128., 576., 530., 420., 390., 142., 400., 127.,
404., 648., 191., 503., 399., 501., 190., 151., 116., 440., 117.,
502., 665., 184., 471., 511., 488., 571., 140., 594., 510., 507.,
504., 447., 643., 395., 585., 586., 568., 627., 577., 537., 449.,
216., 417., 476., 522., 565., 123., 590., 139., 564., 518., 463.,
121., 566., 122., 497., 639., 180., 132., 674., 136., 519., 489.,
144., 521., 491., 573., 506., 421., 614., 165., 482., 540., 546.,
496., 500., 592., 593., 628., 412., 579., 595., 580., 536., 631.,
119., 567., 558., 138., 115., 569., 533., 570., 527., 572., 562.,
534., 644., 189., 498., 517., 637., 505., 126., 642., 553., 515.,
554., 613., 439., 494., 652., 524., 589., 656., 442., 548., 120.,
545., 468., 578., 612., 583., 623., 550., 630., 587., 633., 588.,
563., 615., 661., 617., 551., 529., 625., 601., 560., 600., 581.,
662., 526., 647., 555., 575., 559., 547., 611., 636., 616., 677.,
672., 607., 542., 668., 638., 640., 604., 591., 603., 653., 539.,
114., 655., 646., 667., 654., 543., 678., 659., 597., 582., 671.,
658., 606., 663., 541., 664., 657., 651., 676., 673., 660., 675.,
634., 649., 629., 669., 113., 670., 650., 645., nan])
In [120]:
df_inputs_prep['mth_earliest_cr_line_date_factor'] = pd.cut(df_inputs_prep['mth_earliest_cr_line_date'],50)
In [121]:
df_inputs_prep['mth_earliest_cr_line_date_factor']
Out[121]:
408561 (271.76, 283.1]
70143 (362.48, 373.82]
708782 (373.82, 385.16]
572694 (215.06, 226.4]
774181 (169.7, 181.04]
...
259178 (521.24, 532.58]
365838 (464.54, 475.88]
131932 (237.74, 249.08]
671155 (271.76, 283.1]
121958 (226.4, 237.74]
Name: mth_earliest_cr_line_date_factor, Length: 838860, dtype: category
Categories (50, interval[float64, right]): [(112.433, 124.34] < (124.34, 135.68] < (135.68, 147.02] < (147.02, 158.36] ... (634.64, 645.98] < (645.98, 657.32] < (657.32, 668.66] < (668.66, 680.0]]
In [122]:
df_temp = woe_ordered_continuous(df_inputs_prep, 'mth_earliest_cr_line_date_factor', df_target_prep)
df_temp
C:\Users\computer\AppData\Local\Temp\ipykernel_3596\3982886971.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. df.groupby(df.columns.values[0], as_index=False)[df.columns.values[1]].count(), C:\Users\computer\AppData\Local\Temp\ipykernel_3596\3982886971.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. df.groupby(df.columns.values[0], as_index=False)[df.columns.values[1]].mean()
Out[122]:
| mth_earliest_cr_line_date_factor | n_obs | prop_good | prop_n_obs | good_number | bad_number | prop_good_bor | prop_bad_bor | WoE | IV | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | (112.433, 124.34] | 2799 | 0.973562 | 0.003337 | 2725.0 | 74.0 | 0.003704 | 0.000717 | 1.642161 | 0.016301 |
| 1 | (124.34, 135.68] | 5922 | 0.936170 | 0.007060 | 5544.0 | 378.0 | 0.007536 | 0.003663 | 0.721580 | 0.016301 |
| 2 | (135.68, 147.02] | 7574 | 0.921310 | 0.009029 | 6978.0 | 596.0 | 0.009486 | 0.005775 | 0.496280 | 0.016301 |
| 3 | (147.02, 158.36] | 11325 | 0.885475 | 0.013501 | 10028.0 | 1297.0 | 0.013632 | 0.012567 | 0.081330 | 0.016301 |
| 4 | (158.36, 169.7] | 14728 | 0.878259 | 0.017557 | 12935.0 | 1793.0 | 0.017583 | 0.017373 | 0.012049 | 0.016301 |
| 5 | (169.7, 181.04] | 15388 | 0.873408 | 0.018344 | 13440.0 | 1948.0 | 0.018270 | 0.018875 | -0.032565 | 0.016301 |
| 6 | (181.04, 192.38] | 14484 | 0.877727 | 0.017266 | 12713.0 | 1771.0 | 0.017282 | 0.017160 | 0.007084 | 0.016301 |
| 7 | (192.38, 203.72] | 21080 | 0.880266 | 0.025130 | 18556.0 | 2524.0 | 0.025224 | 0.024455 | 0.030951 | 0.016301 |
| 8 | (203.72, 215.06] | 31896 | 0.881458 | 0.038023 | 28115.0 | 3781.0 | 0.038218 | 0.036635 | 0.042317 | 0.016301 |
| 9 | (215.06, 226.4] | 41926 | 0.884034 | 0.049980 | 37064.0 | 4862.0 | 0.050383 | 0.047109 | 0.067199 | 0.016301 |
| 10 | (226.4, 237.74] | 47612 | 0.874380 | 0.056759 | 41631.0 | 5981.0 | 0.056591 | 0.057951 | -0.023740 | 0.016301 |
| 11 | (237.74, 249.08] | 56171 | 0.865536 | 0.066962 | 48618.0 | 7553.0 | 0.066089 | 0.073182 | -0.101948 | 0.016301 |
| 12 | (249.08, 260.42] | 51736 | 0.868003 | 0.061675 | 44907.0 | 6829.0 | 0.061045 | 0.066167 | -0.080582 | 0.016301 |
| 13 | (260.42, 271.76] | 48603 | 0.863836 | 0.057940 | 41985.0 | 6618.0 | 0.057073 | 0.064123 | -0.116478 | 0.016301 |
| 14 | (271.76, 283.1] | 44839 | 0.863422 | 0.053453 | 38715.0 | 6124.0 | 0.052628 | 0.059336 | -0.119986 | 0.016301 |
| 15 | (283.1, 294.44] | 44632 | 0.867248 | 0.053206 | 38707.0 | 5925.0 | 0.052617 | 0.057408 | -0.087157 | 0.016301 |
| 16 | (294.44, 305.78] | 41182 | 0.871012 | 0.049093 | 35870.0 | 5312.0 | 0.048760 | 0.051469 | -0.054064 | 0.016301 |
| 17 | (305.78, 317.12] | 39703 | 0.876281 | 0.047330 | 34791.0 | 4912.0 | 0.047293 | 0.047593 | -0.006320 | 0.016301 |
| 18 | (317.12, 328.46] | 31047 | 0.872194 | 0.037011 | 27079.0 | 3968.0 | 0.036810 | 0.038447 | -0.043501 | 0.016301 |
| 19 | (328.46, 339.8] | 27466 | 0.877922 | 0.032742 | 24113.0 | 3353.0 | 0.032778 | 0.032488 | 0.008898 | 0.016301 |
| 20 | (339.8, 351.14] | 27548 | 0.875780 | 0.032840 | 24126.0 | 3422.0 | 0.032796 | 0.033156 | -0.010932 | 0.016301 |
| 21 | (351.14, 362.48] | 26016 | 0.875269 | 0.031014 | 22771.0 | 3245.0 | 0.030954 | 0.031441 | -0.015625 | 0.016301 |
| 22 | (362.48, 373.82] | 22949 | 0.879559 | 0.027358 | 20185.0 | 2764.0 | 0.027439 | 0.026781 | 0.024264 | 0.016301 |
| 23 | (373.82, 385.16] | 22110 | 0.887246 | 0.026358 | 19617.0 | 2493.0 | 0.026667 | 0.024155 | 0.098913 | 0.016301 |
| 24 | (385.16, 396.5] | 14218 | 0.882825 | 0.016949 | 12552.0 | 1666.0 | 0.017063 | 0.016142 | 0.055457 | 0.016301 |
| 25 | (396.5, 407.84] | 12431 | 0.882230 | 0.014819 | 10967.0 | 1464.0 | 0.014908 | 0.014185 | 0.049721 | 0.016301 |
| 26 | (407.84, 419.18] | 12681 | 0.889125 | 0.015117 | 11275.0 | 1406.0 | 0.015327 | 0.013623 | 0.117842 | 0.016301 |
| 27 | (419.18, 430.52] | 13038 | 0.890551 | 0.015543 | 11611.0 | 1427.0 | 0.015783 | 0.013826 | 0.132381 | 0.016301 |
| 28 | (430.52, 441.86] | 11776 | 0.888502 | 0.014038 | 10463.0 | 1313.0 | 0.014223 | 0.012722 | 0.111533 | 0.016301 |
| 29 | (441.86, 453.2] | 11076 | 0.888769 | 0.013204 | 9844.0 | 1232.0 | 0.013382 | 0.011937 | 0.114226 | 0.016301 |
| 30 | (453.2, 464.54] | 9056 | 0.888472 | 0.010796 | 8046.0 | 1010.0 | 0.010937 | 0.009786 | 0.111228 | 0.016301 |
| 31 | (464.54, 475.88] | 7786 | 0.891857 | 0.009282 | 6944.0 | 842.0 | 0.009439 | 0.008158 | 0.145856 | 0.016301 |
| 32 | (475.88, 487.22] | 7034 | 0.893091 | 0.008385 | 6282.0 | 752.0 | 0.008539 | 0.007286 | 0.158710 | 0.016301 |
| 33 | (487.22, 498.56] | 6652 | 0.900030 | 0.007930 | 5987.0 | 665.0 | 0.008138 | 0.006443 | 0.233561 | 0.016301 |
| 34 | (498.56, 509.9] | 5410 | 0.889834 | 0.006449 | 4814.0 | 596.0 | 0.006544 | 0.005775 | 0.125046 | 0.016301 |
| 35 | (509.9, 521.24] | 4638 | 0.893920 | 0.005529 | 4146.0 | 492.0 | 0.005636 | 0.004767 | 0.167423 | 0.016301 |
| 36 | (521.24, 532.58] | 3215 | 0.885848 | 0.003833 | 2848.0 | 367.0 | 0.003871 | 0.003556 | 0.085013 | 0.016301 |
| 37 | (532.58, 543.92] | 2347 | 0.892203 | 0.002798 | 2094.0 | 253.0 | 0.002846 | 0.002451 | 0.149445 | 0.016301 |
| 38 | (543.92, 555.26] | 2546 | 0.893951 | 0.003035 | 2276.0 | 270.0 | 0.003094 | 0.002616 | 0.167756 | 0.016301 |
| 39 | (555.26, 566.6] | 2498 | 0.890312 | 0.002978 | 2224.0 | 274.0 | 0.003023 | 0.002655 | 0.129937 | 0.016301 |
| 40 | (566.6, 577.94] | 2441 | 0.885293 | 0.002910 | 2161.0 | 280.0 | 0.002938 | 0.002713 | 0.079540 | 0.016301 |
| 41 | (577.94, 589.28] | 2003 | 0.894159 | 0.002388 | 1791.0 | 212.0 | 0.002435 | 0.002054 | 0.169946 | 0.016301 |
| 42 | (589.28, 600.62] | 1521 | 0.889546 | 0.001813 | 1353.0 | 168.0 | 0.001839 | 0.001628 | 0.122118 | 0.016301 |
| 43 | (600.62, 611.96] | 1178 | 0.885399 | 0.001404 | 1043.0 | 135.0 | 0.001418 | 0.001308 | 0.080584 | 0.016301 |
| 44 | (611.96, 623.3] | 1189 | 0.882254 | 0.001417 | 1049.0 | 140.0 | 0.001426 | 0.001356 | 0.049953 | 0.016301 |
| 45 | (623.3, 634.64] | 868 | 0.898618 | 0.001035 | 780.0 | 88.0 | 0.001060 | 0.000853 | 0.217960 | 0.016301 |
| 46 | (634.64, 645.98] | 838 | 0.869928 | 0.000999 | 729.0 | 109.0 | 0.000991 | 0.001056 | -0.063671 | 0.016301 |
| 47 | (645.98, 657.32] | 628 | 0.880573 | 0.000749 | 553.0 | 75.0 | 0.000752 | 0.000727 | 0.033873 | 0.016301 |
| 48 | (657.32, 668.66] | 576 | 0.878472 | 0.000687 | 506.0 | 70.0 | 0.000688 | 0.000678 | 0.014044 | 0.016301 |
| 49 | (668.66, 680.0] | 2470 | 0.846559 | 0.002945 | 2091.0 | 379.0 | 0.002842 | 0.003672 | -0.256136 | 0.016301 |
In [123]:
plot_by_woe(df_temp)
In [124]:
plot_by_woe(df_temp,90)
In [125]:
df_inputs_prep['mth_earliest_cr_line_date_factor:111-134'] = np.where(df_inputs_prep['mth_earliest_cr_line_date_factor'].isin(range(111,135)),1,0)
df_inputs_prep['mth_earliest_cr_line_date_factor:135-156'] = np.where(df_inputs_prep['mth_earliest_cr_line_date_factor'].isin(range(135,157)),1,0)
df_inputs_prep['mth_earliest_cr_line_date_factor:157-414'] = np.where(df_inputs_prep['mth_earliest_cr_line_date_factor'].isin(range(157,415)),1,0)
df_inputs_prep['mth_earliest_cr_line_date_factor:415-515'] = np.where(df_inputs_prep['mth_earliest_cr_line_date_factor'].isin(range(415,516)),1,0)
df_inputs_prep['mth_earliest_cr_line_date_factor:516-671'] = np.where(df_inputs_prep['mth_earliest_cr_line_date_factor'].isin(range(517,672)),1,0)
In [126]:
df_inputs_prep['int_rate_factor'] = pd.cut(df_inputs_prep['int_rate'],50)
In [127]:
df_temp = woe_ordered_continuous(df_inputs_prep, 'int_rate_factor', df_target_prep)
df_temp
C:\Users\computer\AppData\Local\Temp\ipykernel_3596\3982886971.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. df.groupby(df.columns.values[0], as_index=False)[df.columns.values[1]].count(), C:\Users\computer\AppData\Local\Temp\ipykernel_3596\3982886971.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. df.groupby(df.columns.values[0], as_index=False)[df.columns.values[1]].mean()
Out[127]:
| int_rate_factor | n_obs | prop_good | prop_n_obs | good_number | bad_number | prop_good_bor | prop_bad_bor | WoE | IV | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | (5.284, 5.824] | 27367 | 0.977382 | 0.032624 | 26748.0 | 619.0 | 0.036360 | 0.005998 | 1.802112 | 0.498952 |
| 1 | (5.824, 6.337] | 19927 | 0.982888 | 0.023755 | 19586.0 | 341.0 | 0.026624 | 0.003304 | 2.086691 | 0.498952 |
| 2 | (6.337, 6.851] | 18129 | 0.974902 | 0.021612 | 17674.0 | 455.0 | 0.024025 | 0.004409 | 1.695555 | 0.498952 |
| 3 | (6.851, 7.364] | 45751 | 0.963804 | 0.054540 | 44095.0 | 1656.0 | 0.059941 | 0.016045 | 1.317944 | 0.498952 |
| 4 | (7.364, 7.878] | 15221 | 0.964194 | 0.018145 | 14676.0 | 545.0 | 0.019950 | 0.005281 | 1.329186 | 0.498952 |
| 5 | (7.878, 8.392] | 60872 | 0.928834 | 0.072566 | 56540.0 | 4332.0 | 0.076858 | 0.041973 | 0.604922 | 0.498952 |
| 6 | (8.392, 8.905] | 7718 | 0.968904 | 0.009201 | 7478.0 | 240.0 | 0.010165 | 0.002325 | 1.475085 | 0.498952 |
| 7 | (8.905, 9.419] | 23119 | 0.897660 | 0.027560 | 20753.0 | 2366.0 | 0.028211 | 0.022925 | 0.207493 | 0.498952 |
| 8 | (9.419, 9.932] | 40558 | 0.943340 | 0.048350 | 38260.0 | 2298.0 | 0.052009 | 0.022266 | 0.848369 | 0.498952 |
| 9 | (9.932, 10.446] | 34946 | 0.928633 | 0.041659 | 32452.0 | 2494.0 | 0.044114 | 0.024165 | 0.601877 | 0.498952 |
| 10 | (10.446, 10.96] | 32259 | 0.927121 | 0.038456 | 29908.0 | 2351.0 | 0.040656 | 0.022779 | 0.579288 | 0.498952 |
| 11 | (10.96, 11.473] | 36385 | 0.888608 | 0.043375 | 32332.0 | 4053.0 | 0.043951 | 0.039270 | 0.112603 | 0.498952 |
| 12 | (11.473, 11.987] | 27279 | 0.903992 | 0.032520 | 24660.0 | 2619.0 | 0.033522 | 0.025376 | 0.278393 | 0.498952 |
| 13 | (11.987, 12.5] | 43999 | 0.867338 | 0.052452 | 38162.0 | 5837.0 | 0.051876 | 0.056556 | -0.086374 | 0.498952 |
| 14 | (12.5, 13.014] | 51398 | 0.873633 | 0.061272 | 44903.0 | 6495.0 | 0.061039 | 0.062931 | -0.030525 | 0.498952 |
| 15 | (13.014, 13.528] | 21058 | 0.818454 | 0.025103 | 17235.0 | 3823.0 | 0.023429 | 0.037042 | -0.458090 | 0.498952 |
| 16 | (13.528, 14.041] | 48260 | 0.858413 | 0.057531 | 41427.0 | 6833.0 | 0.056314 | 0.066206 | -0.161828 | 0.498952 |
| 17 | (14.041, 14.555] | 30671 | 0.884027 | 0.036563 | 27114.0 | 3557.0 | 0.036858 | 0.034464 | 0.067135 | 0.498952 |
| 18 | (14.555, 15.068] | 34834 | 0.872854 | 0.041526 | 30405.0 | 4429.0 | 0.041331 | 0.042913 | -0.037564 | 0.498952 |
| 19 | (15.068, 15.582] | 7744 | 0.777118 | 0.009232 | 6018.0 | 1726.0 | 0.008181 | 0.016724 | -0.715049 | 0.498952 |
| 20 | (15.582, 16.096] | 31498 | 0.836021 | 0.037549 | 26333.0 | 5165.0 | 0.035796 | 0.050045 | -0.335079 | 0.498952 |
| 21 | (16.096, 16.609] | 19065 | 0.797377 | 0.022728 | 15202.0 | 3863.0 | 0.020665 | 0.037429 | -0.594014 | 0.498952 |
| 22 | (16.609, 17.123] | 18972 | 0.831910 | 0.022617 | 15783.0 | 3189.0 | 0.021455 | 0.030899 | -0.364771 | 0.498952 |
| 23 | (17.123, 17.636] | 13638 | 0.736618 | 0.016258 | 10046.0 | 3592.0 | 0.013656 | 0.034804 | -0.935532 | 0.498952 |
| 24 | (17.636, 18.15] | 18343 | 0.810718 | 0.021867 | 14871.0 | 3472.0 | 0.020215 | 0.033641 | -0.509315 | 0.498952 |
| 25 | (18.15, 18.664] | 15545 | 0.707494 | 0.018531 | 10998.0 | 4547.0 | 0.014950 | 0.044057 | -1.080751 | 0.498952 |
| 26 | (18.664, 19.177] | 15739 | 0.827753 | 0.018763 | 13028.0 | 2711.0 | 0.017710 | 0.026267 | -0.394214 | 0.498952 |
| 27 | (19.177, 19.691] | 7620 | 0.709711 | 0.009084 | 5408.0 | 2212.0 | 0.007351 | 0.021432 | -1.070015 | 0.498952 |
| 28 | (19.691, 20.204] | 13156 | 0.785801 | 0.015683 | 10338.0 | 2818.0 | 0.014053 | 0.027304 | -0.664198 | 0.498952 |
| 29 | (20.204, 20.718] | 2144 | 0.776119 | 0.002556 | 1664.0 | 480.0 | 0.002262 | 0.004651 | -0.720804 | 0.498952 |
| 30 | (20.718, 21.232] | 8010 | 0.758302 | 0.009549 | 6074.0 | 1936.0 | 0.008257 | 0.018758 | -0.820604 | 0.498952 |
| 31 | (21.232, 21.745] | 5504 | 0.801599 | 0.006561 | 4412.0 | 1092.0 | 0.005997 | 0.010581 | -0.567680 | 0.498952 |
| 32 | (21.745, 22.259] | 4004 | 0.691808 | 0.004773 | 2770.0 | 1234.0 | 0.003765 | 0.011956 | -1.155411 | 0.498952 |
| 33 | (22.259, 22.772] | 4456 | 0.844479 | 0.005312 | 3763.0 | 693.0 | 0.005115 | 0.006715 | -0.272055 | 0.498952 |
| 34 | (22.772, 23.286] | 3716 | 0.651776 | 0.004430 | 2422.0 | 1294.0 | 0.003292 | 0.012538 | -1.337142 | 0.498952 |
| 35 | (23.286, 23.8] | 1135 | 0.881057 | 0.001353 | 1000.0 | 135.0 | 0.001359 | 0.001308 | 0.038483 | 0.498952 |
| 36 | (23.8, 24.313] | 4895 | 0.703166 | 0.005835 | 3442.0 | 1453.0 | 0.004679 | 0.014078 | -1.101575 | 0.498952 |
| 37 | (24.313, 24.827] | 1421 | 0.802252 | 0.001694 | 1140.0 | 281.0 | 0.001550 | 0.002723 | -0.563568 | 0.498952 |
| 38 | (24.827, 25.34] | 4758 | 0.737915 | 0.005672 | 3511.0 | 1247.0 | 0.004773 | 0.012082 | -0.928837 | 0.498952 |
| 39 | (25.34, 25.854] | 3441 | 0.705318 | 0.004102 | 2427.0 | 1014.0 | 0.003299 | 0.009825 | -1.091244 | 0.498952 |
| 40 | (25.854, 26.368] | 4255 | 0.760517 | 0.005072 | 3236.0 | 1019.0 | 0.004399 | 0.009873 | -0.808481 | 0.498952 |
| 41 | (26.368, 26.881] | 1034 | 0.645068 | 0.001233 | 667.0 | 367.0 | 0.000907 | 0.003556 | -1.366569 | 0.498952 |
| 42 | (26.881, 27.395] | 1516 | 0.812665 | 0.001807 | 1232.0 | 284.0 | 0.001675 | 0.002752 | -0.496577 | 0.498952 |
| 43 | (27.395, 27.908] | 248 | 0.508065 | 0.000296 | 126.0 | 122.0 | 0.000171 | 0.001182 | -1.931736 | 0.498952 |
| 44 | (27.908, 28.422] | 334 | 0.562874 | 0.000398 | 188.0 | 146.0 | 0.000256 | 0.001415 | -1.711162 | 0.498952 |
| 45 | (28.422, 28.936] | 1453 | 0.763937 | 0.001732 | 1110.0 | 343.0 | 0.001509 | 0.003323 | -0.789612 | 0.498952 |
| 46 | (28.936, 29.449] | 186 | 0.446237 | 0.000222 | 83.0 | 103.0 | 0.000113 | 0.000998 | -2.179886 | 0.498952 |
| 47 | (29.449, 29.963] | 846 | 0.762411 | 0.001009 | 645.0 | 201.0 | 0.000877 | 0.001948 | -0.798052 | 0.498952 |
| 48 | (29.963, 30.476] | 826 | 0.789346 | 0.000985 | 652.0 | 174.0 | 0.000886 | 0.001686 | -0.643008 | 0.498952 |
| 49 | (30.476, 30.99] | 3597 | 0.735335 | 0.004288 | 2645.0 | 952.0 | 0.003595 | 0.009224 | -0.942136 | 0.498952 |
In [128]:
plot_by_woe(df_temp,90)
In [129]:
df_inputs_prep['int_rate:<16.609'] = np.where((df_inputs_prep['int_rate'] <=16.609),1,0)
df_inputs_prep['int_rate:16.609-19.177'] = np.where((df_inputs_prep['int_rate'] >16.609) & (df_inputs_prep['int_rate'] < 19.177),1,0)
df_inputs_prep['int_rate:19.177-30.99'] = np.where((df_inputs_prep['int_rate'] >19.177) & (df_inputs_prep['int_rate'] < 30.99),1,0)
In [130]:
df_inputs_prep['funded_amnt_factor'] = pd.cut(df_inputs_prep['funded_amnt'],50)
In [131]:
df_temp = woe_ordered_continuous(df_inputs_prep, 'funded_amnt_factor', df_target_prep)
df_temp
C:\Users\computer\AppData\Local\Temp\ipykernel_3596\3982886971.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. df.groupby(df.columns.values[0], as_index=False)[df.columns.values[1]].count(), C:\Users\computer\AppData\Local\Temp\ipykernel_3596\3982886971.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. df.groupby(df.columns.values[0], as_index=False)[df.columns.values[1]].mean()
Out[131]:
| funded_amnt_factor | n_obs | prop_good | prop_n_obs | good_number | bad_number | prop_good_bor | prop_bad_bor | WoE | IV | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | (961.0, 1780.0] | 9171 | 0.919093 | 0.010933 | 8429.0 | 742.0 | 0.011458 | 0.007189 | 0.466087 | 0.043982 |
| 1 | (1780.0, 2560.0] | 14667 | 0.904889 | 0.017485 | 13272.0 | 1395.0 | 0.018041 | 0.013516 | 0.288765 | 0.043982 |
| 2 | (2560.0, 3340.0] | 16724 | 0.901160 | 0.019937 | 15071.0 | 1653.0 | 0.020487 | 0.016016 | 0.246183 | 0.043982 |
| 3 | (3340.0, 4120.0] | 21714 | 0.902920 | 0.025885 | 19606.0 | 2108.0 | 0.026652 | 0.020425 | 0.266099 | 0.043982 |
| 4 | (4120.0, 4900.0] | 13850 | 0.882744 | 0.016511 | 12226.0 | 1624.0 | 0.016619 | 0.015735 | 0.054675 | 0.043982 |
| 5 | (4900.0, 5680.0] | 42275 | 0.893601 | 0.050396 | 37777.0 | 4498.0 | 0.051352 | 0.043582 | 0.164070 | 0.043982 |
| 6 | (5680.0, 6460.0] | 35407 | 0.890558 | 0.042209 | 31532.0 | 3875.0 | 0.042863 | 0.037546 | 0.132460 | 0.043982 |
| 7 | (6460.0, 7240.0] | 27837 | 0.889643 | 0.033185 | 24765.0 | 3072.0 | 0.033664 | 0.029765 | 0.123105 | 0.043982 |
| 8 | (7240.0, 8020.0] | 38194 | 0.883804 | 0.045531 | 33756.0 | 4438.0 | 0.045886 | 0.043001 | 0.064957 | 0.043982 |
| 9 | (8020.0, 8800.0] | 12018 | 0.863372 | 0.014327 | 10376.0 | 1642.0 | 0.014105 | 0.015910 | -0.120417 | 0.043982 |
| 10 | (8800.0, 9580.0] | 15183 | 0.878680 | 0.018100 | 13341.0 | 1842.0 | 0.018135 | 0.017847 | 0.015993 | 0.043982 |
| 11 | (9580.0, 10360.0] | 79900 | 0.889574 | 0.095249 | 71077.0 | 8823.0 | 0.096619 | 0.085488 | 0.122405 | 0.043982 |
| 12 | (10360.0, 11140.0] | 17064 | 0.864510 | 0.020342 | 14752.0 | 2312.0 | 0.020053 | 0.022401 | -0.110732 | 0.043982 |
| 13 | (11140.0, 11920.0] | 10480 | 0.820038 | 0.012493 | 8594.0 | 1886.0 | 0.011682 | 0.018274 | -0.447391 | 0.043982 |
| 14 | (11920.0, 12700.0] | 53337 | 0.874515 | 0.063583 | 46644.0 | 6693.0 | 0.063406 | 0.064850 | -0.022515 | 0.043982 |
| 15 | (12700.0, 13480.0] | 14371 | 0.869598 | 0.017132 | 12497.0 | 1874.0 | 0.016988 | 0.018158 | -0.066584 | 0.043982 |
| 16 | (13480.0, 14260.0] | 18682 | 0.860400 | 0.022271 | 16074.0 | 2608.0 | 0.021850 | 0.025269 | -0.145378 | 0.043982 |
| 17 | (14260.0, 15040.0] | 56316 | 0.873411 | 0.067135 | 49187.0 | 7129.0 | 0.066863 | 0.069074 | -0.032539 | 0.043982 |
| 18 | (15040.0, 15820.0] | 6248 | 0.823784 | 0.007448 | 5147.0 | 1101.0 | 0.006997 | 0.010668 | -0.421802 | 0.043982 |
| 19 | (15820.0, 16600.0] | 29518 | 0.868080 | 0.035189 | 25624.0 | 3894.0 | 0.034832 | 0.037730 | -0.079905 | 0.043982 |
| 20 | (16600.0, 17380.0] | 11034 | 0.854269 | 0.013154 | 9426.0 | 1608.0 | 0.012813 | 0.015580 | -0.195517 | 0.043982 |
| 21 | (17380.0, 18160.0] | 23846 | 0.858215 | 0.028427 | 20465.0 | 3381.0 | 0.027819 | 0.032759 | -0.163453 | 0.043982 |
| 22 | (18160.0, 18940.0] | 4626 | 0.802853 | 0.005515 | 3714.0 | 912.0 | 0.005049 | 0.008837 | -0.559772 | 0.043982 |
| 23 | (18940.0, 19720.0] | 9025 | 0.847535 | 0.010759 | 7649.0 | 1376.0 | 0.010398 | 0.013332 | -0.248603 | 0.043982 |
| 24 | (19720.0, 20500.0] | 54403 | 0.874327 | 0.064854 | 47566.0 | 6837.0 | 0.064659 | 0.066245 | -0.024228 | 0.043982 |
| 25 | (20500.0, 21280.0] | 13065 | 0.839648 | 0.015575 | 10970.0 | 2095.0 | 0.014912 | 0.020299 | -0.308386 | 0.043982 |
| 26 | (21280.0, 22060.0] | 8712 | 0.860078 | 0.010386 | 7493.0 | 1219.0 | 0.010186 | 0.011811 | -0.148059 | 0.043982 |
| 27 | (22060.0, 22840.0] | 4181 | 0.822052 | 0.004984 | 3437.0 | 744.0 | 0.004672 | 0.007209 | -0.433684 | 0.043982 |
| 28 | (22840.0, 23620.0] | 5165 | 0.847241 | 0.006157 | 4376.0 | 789.0 | 0.005949 | 0.007645 | -0.250873 | 0.043982 |
| 29 | (23620.0, 24400.0] | 25224 | 0.864018 | 0.030070 | 21794.0 | 3430.0 | 0.029626 | 0.033234 | -0.114923 | 0.043982 |
| 30 | (24400.0, 25180.0] | 27898 | 0.886874 | 0.033257 | 24742.0 | 3156.0 | 0.033633 | 0.030579 | 0.095200 | 0.043982 |
| 31 | (25180.0, 25960.0] | 2950 | 0.840000 | 0.003517 | 2478.0 | 472.0 | 0.003368 | 0.004573 | -0.305769 | 0.043982 |
| 32 | (25960.0, 26740.0] | 4227 | 0.864916 | 0.005039 | 3656.0 | 571.0 | 0.004970 | 0.005533 | -0.107261 | 0.043982 |
| 33 | (26740.0, 27520.0] | 4038 | 0.868252 | 0.004814 | 3506.0 | 532.0 | 0.004766 | 0.005155 | -0.078410 | 0.043982 |
| 34 | (27520.0, 28300.0] | 13943 | 0.878649 | 0.016622 | 12251.0 | 1692.0 | 0.016653 | 0.016394 | 0.015699 | 0.043982 |
| 35 | (28300.0, 29080.0] | 2954 | 0.851726 | 0.003521 | 2516.0 | 438.0 | 0.003420 | 0.004244 | -0.215791 | 0.043982 |
| 36 | (29080.0, 29860.0] | 1586 | 0.827869 | 0.001891 | 1313.0 | 273.0 | 0.001785 | 0.002645 | -0.393399 | 0.043982 |
| 37 | (29860.0, 30640.0] | 24655 | 0.881484 | 0.029391 | 21733.0 | 2922.0 | 0.029543 | 0.028312 | 0.042566 | 0.043982 |
| 38 | (30640.0, 31420.0] | 1838 | 0.853645 | 0.002191 | 1569.0 | 269.0 | 0.002133 | 0.002606 | -0.200515 | 0.043982 |
| 39 | (31420.0, 32200.0] | 6722 | 0.882029 | 0.008013 | 5929.0 | 793.0 | 0.008060 | 0.007684 | 0.047790 | 0.043982 |
| 40 | (32200.0, 32980.0] | 1247 | 0.831596 | 0.001487 | 1037.0 | 210.0 | 0.001410 | 0.002035 | -0.367018 | 0.043982 |
| 41 | (32980.0, 33760.0] | 2264 | 0.864841 | 0.002699 | 1958.0 | 306.0 | 0.002662 | 0.002965 | -0.107903 | 0.043982 |
| 42 | (33760.0, 34540.0] | 1474 | 0.852103 | 0.001757 | 1256.0 | 218.0 | 0.001707 | 0.002112 | -0.212805 | 0.043982 |
| 43 | (34540.0, 35320.0] | 34088 | 0.852881 | 0.040637 | 29073.0 | 5015.0 | 0.039521 | 0.048591 | -0.206621 | 0.043982 |
| 44 | (35320.0, 36100.0] | 2529 | 0.950969 | 0.003015 | 2405.0 | 124.0 | 0.003269 | 0.001201 | 1.001026 | 0.043982 |
| 45 | (36100.0, 36880.0] | 243 | 0.946502 | 0.000290 | 230.0 | 13.0 | 0.000313 | 0.000126 | 0.909133 | 0.043982 |
| 46 | (36880.0, 37660.0] | 465 | 0.959140 | 0.000554 | 446.0 | 19.0 | 0.000606 | 0.000184 | 1.191883 | 0.043982 |
| 47 | (37660.0, 38440.0] | 573 | 0.968586 | 0.000683 | 555.0 | 18.0 | 0.000754 | 0.000174 | 1.464599 | 0.043982 |
| 48 | (38440.0, 39220.0] | 319 | 0.968652 | 0.000380 | 309.0 | 10.0 | 0.000420 | 0.000097 | 1.466759 | 0.043982 |
| 49 | (39220.0, 40000.0] | 12600 | 0.955794 | 0.015021 | 12043.0 | 557.0 | 0.016371 | 0.005397 | 1.109676 | 0.043982 |
In [132]:
plot_by_woe(df_temp,90)
In [133]:
df_inputs_prep['installment_factor'] = pd.cut(df_inputs_prep['installment'],50)
In [134]:
df_temp = woe_ordered_continuous(df_inputs_prep, 'installment_factor', df_target_prep)
df_temp
C:\Users\computer\AppData\Local\Temp\ipykernel_3596\3982886971.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. df.groupby(df.columns.values[0], as_index=False)[df.columns.values[1]].count(), C:\Users\computer\AppData\Local\Temp\ipykernel_3596\3982886971.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. df.groupby(df.columns.values[0], as_index=False)[df.columns.values[1]].mean()
Out[134]:
| installment_factor | n_obs | prop_good | prop_n_obs | good_number | bad_number | prop_good_bor | prop_bad_bor | WoE | IV | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | (13.065, 48.871] | 6124 | 0.925049 | 0.007300 | 5665.0 | 459.0 | 0.007701 | 0.004447 | 0.549015 | inf |
| 1 | (48.871, 82.972] | 14850 | 0.912458 | 0.017703 | 13550.0 | 1300.0 | 0.018419 | 0.012596 | 0.380025 | inf |
| 2 | (82.972, 117.074] | 22671 | 0.906797 | 0.027026 | 20558.0 | 2113.0 | 0.027946 | 0.020473 | 0.311144 | inf |
| 3 | (117.074, 151.175] | 25471 | 0.903027 | 0.030364 | 23001.0 | 2470.0 | 0.031267 | 0.023932 | 0.267322 | inf |
| 4 | (151.175, 185.276] | 47537 | 0.897448 | 0.056669 | 42662.0 | 4875.0 | 0.057993 | 0.047235 | 0.205191 | inf |
| 5 | (185.276, 219.377] | 44863 | 0.896418 | 0.053482 | 40216.0 | 4647.0 | 0.054668 | 0.045026 | 0.194046 | inf |
| 6 | (219.377, 253.478] | 47513 | 0.889988 | 0.056641 | 42286.0 | 5227.0 | 0.057482 | 0.050645 | 0.126621 | inf |
| 7 | (253.478, 287.58] | 55509 | 0.864058 | 0.066173 | 47963.0 | 7546.0 | 0.065199 | 0.073114 | -0.114585 | inf |
| 8 | (287.58, 321.681] | 58512 | 0.885186 | 0.069753 | 51794.0 | 6718.0 | 0.070407 | 0.065092 | 0.078487 | inf |
| 9 | (321.681, 355.782] | 59983 | 0.864762 | 0.071506 | 51871.0 | 8112.0 | 0.070511 | 0.078599 | -0.108582 | inf |
| 10 | (355.782, 389.883] | 50642 | 0.872853 | 0.060371 | 44203.0 | 6439.0 | 0.060088 | 0.062389 | -0.037578 | inf |
| 11 | (389.883, 423.984] | 35546 | 0.856974 | 0.042375 | 30462.0 | 5084.0 | 0.041409 | 0.049260 | -0.173616 | inf |
| 12 | (423.984, 458.086] | 30210 | 0.870871 | 0.036014 | 26309.0 | 3901.0 | 0.035763 | 0.037797 | -0.055319 | inf |
| 13 | (458.086, 492.187] | 42665 | 0.877323 | 0.050861 | 37431.0 | 5234.0 | 0.050882 | 0.050713 | 0.003326 | inf |
| 14 | (492.187, 526.288] | 33948 | 0.854542 | 0.040470 | 29010.0 | 4938.0 | 0.039435 | 0.047845 | -0.193317 | inf |
| 15 | (526.288, 560.389] | 28593 | 0.854790 | 0.034086 | 24441.0 | 4152.0 | 0.033224 | 0.040229 | -0.191325 | inf |
| 16 | (560.389, 594.49] | 22973 | 0.854177 | 0.027386 | 19623.0 | 3350.0 | 0.026675 | 0.032459 | -0.196255 | inf |
| 17 | (594.49, 628.592] | 25451 | 0.890024 | 0.030340 | 22652.0 | 2799.0 | 0.030792 | 0.027120 | 0.126989 | inf |
| 18 | (628.592, 662.693] | 24432 | 0.871071 | 0.029126 | 21282.0 | 3150.0 | 0.028930 | 0.030521 | -0.053538 | inf |
| 19 | (662.693, 696.794] | 20672 | 0.851974 | 0.024643 | 17612.0 | 3060.0 | 0.023941 | 0.029649 | -0.213832 | inf |
| 20 | (696.794, 730.895] | 13216 | 0.860926 | 0.015755 | 11378.0 | 1838.0 | 0.015467 | 0.017809 | -0.140994 | inf |
| 21 | (730.895, 764.996] | 16154 | 0.890058 | 0.019257 | 14378.0 | 1776.0 | 0.019545 | 0.017208 | 0.127338 | inf |
| 22 | (764.996, 799.098] | 17256 | 0.885199 | 0.020571 | 15275.0 | 1981.0 | 0.020764 | 0.019194 | 0.078619 | inf |
| 23 | (799.098, 833.199] | 11634 | 0.875107 | 0.013869 | 10181.0 | 1453.0 | 0.013840 | 0.014078 | -0.017104 | inf |
| 24 | (833.199, 867.3] | 12137 | 0.882096 | 0.014469 | 10706.0 | 1431.0 | 0.014553 | 0.013865 | 0.048434 | inf |
| 25 | (867.3, 901.401] | 10019 | 0.842100 | 0.011944 | 8437.0 | 1582.0 | 0.011469 | 0.015328 | -0.290060 | inf |
| 26 | (901.401, 935.502] | 8301 | 0.883388 | 0.009896 | 7333.0 | 968.0 | 0.009968 | 0.009379 | 0.060911 | inf |
| 27 | (935.502, 969.604] | 6985 | 0.862706 | 0.008327 | 6026.0 | 959.0 | 0.008191 | 0.009292 | -0.126050 | inf |
| 28 | (969.604, 1003.705] | 5985 | 0.872180 | 0.007135 | 5220.0 | 765.0 | 0.007096 | 0.007412 | -0.043620 | inf |
| 29 | (1003.705, 1037.806] | 4020 | 0.833831 | 0.004792 | 3352.0 | 668.0 | 0.004557 | 0.006472 | -0.350973 | inf |
| 30 | (1037.806, 1071.907] | 4217 | 0.857956 | 0.005027 | 3618.0 | 599.0 | 0.004918 | 0.005804 | -0.165582 | inf |
| 31 | (1071.907, 1106.008] | 6122 | 0.909833 | 0.007298 | 5570.0 | 552.0 | 0.007572 | 0.005348 | 0.347605 | inf |
| 32 | (1106.008, 1140.11] | 3996 | 0.903403 | 0.004764 | 3610.0 | 386.0 | 0.004907 | 0.003740 | 0.271628 | inf |
| 33 | (1140.11, 1174.211] | 4323 | 0.865140 | 0.005153 | 3740.0 | 583.0 | 0.005084 | 0.005649 | -0.105344 | inf |
| 34 | (1174.211, 1208.312] | 4485 | 0.871126 | 0.005347 | 3907.0 | 578.0 | 0.005311 | 0.005600 | -0.053046 | inf |
| 35 | (1208.312, 1242.413] | 4301 | 0.914671 | 0.005127 | 3934.0 | 367.0 | 0.005348 | 0.003556 | 0.408053 | inf |
| 36 | (1242.413, 1276.514] | 2839 | 0.839380 | 0.003384 | 2383.0 | 456.0 | 0.003239 | 0.004418 | -0.310375 | inf |
| 37 | (1276.514, 1310.616] | 2203 | 0.875170 | 0.002626 | 1928.0 | 275.0 | 0.002621 | 0.002665 | -0.016530 | inf |
| 38 | (1310.616, 1344.717] | 1071 | 0.859010 | 0.001277 | 920.0 | 151.0 | 0.001251 | 0.001463 | -0.156903 | inf |
| 39 | (1344.717, 1378.818] | 575 | 0.801739 | 0.000685 | 461.0 | 114.0 | 0.000627 | 0.001105 | -0.566798 | inf |
| 40 | (1378.818, 1412.919] | 399 | 0.844612 | 0.000476 | 337.0 | 62.0 | 0.000458 | 0.000601 | -0.271049 | inf |
| 41 | (1412.919, 1447.02] | 138 | 0.840580 | 0.000165 | 116.0 | 22.0 | 0.000158 | 0.000213 | -0.301449 | inf |
| 42 | (1447.02, 1481.122] | 110 | 0.863636 | 0.000131 | 95.0 | 15.0 | 0.000129 | 0.000145 | -0.118171 | inf |
| 43 | (1481.122, 1515.223] | 118 | 0.711864 | 0.000141 | 84.0 | 34.0 | 0.000114 | 0.000329 | -1.059541 | inf |
| 44 | (1515.223, 1549.324] | 42 | 0.761905 | 0.000050 | 32.0 | 10.0 | 0.000043 | 0.000097 | -0.800846 | inf |
| 45 | (1549.324, 1583.425] | 15 | 0.666667 | 0.000018 | 10.0 | 5.0 | 0.000014 | 0.000048 | -1.270850 | inf |
| 46 | (1583.425, 1617.526] | 11 | 0.818182 | 0.000013 | 9.0 | 2.0 | 0.000012 | 0.000019 | -0.459920 | inf |
| 47 | (1617.526, 1651.628] | 5 | 1.000000 | 0.000006 | 5.0 | 0.0 | 0.000007 | 0.000000 | inf | inf |
| 48 | (1651.628, 1685.729] | 1 | 1.000000 | 0.000001 | 1.0 | 0.0 | 0.000001 | 0.000000 | inf | inf |
| 49 | (1685.729, 1719.83] | 7 | 0.714286 | 0.000008 | 5.0 | 2.0 | 0.000007 | 0.000019 | -1.047706 | inf |
In [135]:
plot_by_woe(df_temp,90)
In [136]:
import numpy as np
df_inputs_prep['installment_factor:13-321'] = np.where(df_inputs_prep['installment_factor'].isin(range(13, 322)), 1, 0)
df_inputs_prep['installment_factor:322-1310'] = np.where(df_inputs_prep['installment_factor'].isin(range(322, 1311)), 1, 0)
df_inputs_prep['installment_factor:1311-1719'] = np.where(df_inputs_prep['installment_factor'].isin(range(1311, 1720)), 1, 0)
In [137]:
df_inputs_prep['annual_inc_factor'] = pd.cut(df_inputs_prep['annual_inc'],1000)
In [138]:
df_temp = woe_ordered_continuous(df_inputs_prep, 'annual_inc_factor', df_target_prep)
df_temp
C:\Users\computer\AppData\Local\Temp\ipykernel_3596\3982886971.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. df.groupby(df.columns.values[0], as_index=False)[df.columns.values[1]].count(), C:\Users\computer\AppData\Local\Temp\ipykernel_3596\3982886971.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. df.groupby(df.columns.values[0], as_index=False)[df.columns.values[1]].mean() C:\Users\computer\Music\python\Lib\site-packages\pandas\core\arraylike.py:399: RuntimeWarning: divide by zero encountered in log result = getattr(ufunc, method)(*inputs, **kwargs)
Out[138]:
| annual_inc_factor | n_obs | prop_good | prop_n_obs | good_number | bad_number | prop_good_bor | prop_bad_bor | WoE | IV | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | (-110000.0, 110000.0] | 706374 | 0.870663 | 0.842074 | 615014.0 | 91360.0 | 0.836024 | 0.885203 | -0.057160 | inf |
| 1 | (110000.0, 220000.0] | 116588 | 0.908696 | 0.138986 | 105943.0 | 10645.0 | 0.144014 | 0.103141 | 0.333814 | inf |
| 2 | (220000.0, 330000.0] | 11377 | 0.920541 | 0.013563 | 10473.0 | 904.0 | 0.014237 | 0.008759 | 0.485729 | inf |
| 3 | (330000.0, 440000.0] | 2462 | 0.930544 | 0.002935 | 2291.0 | 171.0 | 0.003114 | 0.001657 | 0.631083 | inf |
| 4 | (440000.0, 550000.0] | 1043 | 0.938639 | 0.001243 | 979.0 | 64.0 | 0.001331 | 0.000620 | 0.763651 | inf |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 995 | (109450000.0, 109560000.0] | 0 | NaN | 0.000000 | NaN | NaN | NaN | NaN | NaN | inf |
| 996 | (109560000.0, 109670000.0] | 0 | NaN | 0.000000 | NaN | NaN | NaN | NaN | NaN | inf |
| 997 | (109670000.0, 109780000.0] | 0 | NaN | 0.000000 | NaN | NaN | NaN | NaN | NaN | inf |
| 998 | (109780000.0, 109890000.0] | 0 | NaN | 0.000000 | NaN | NaN | NaN | NaN | NaN | inf |
| 999 | (109890000.0, 110000000.0] | 1 | 1.000000 | 0.000001 | 1.0 | 0.0 | 0.000001 | 0.000000 | inf | inf |
1000 rows × 10 columns
In [139]:
plot_by_woe(df_temp,90)
In [140]:
df_inputs_prep['annual_inc:<110000.0'] = np.where((df_inputs_prep['annual_inc'] <=100000.0),1,0)
df_inputs_prep['annual_inc:110000.0-220000.0'] = np.where((df_inputs_prep['annual_inc'] >110000.0) & (df_inputs_prep['annual_inc'] < 220000.0),1,0)
df_inputs_prep['annual_inc:220000.0-330000.0'] = np.where((df_inputs_prep['int_rate'] >220000.0) & (df_inputs_prep['int_rate'] < 330000.0),1,0)
df_inputs_prep['annual_inc:330000.0-440000.0'] = np.where((df_inputs_prep['int_rate'] >330000.0) & (df_inputs_prep['int_rate'] < 440000.0),1,0)
df_inputs_prep['annual.inc:440000.0-550000.0'] = np.where((df_inputs_prep['int_rate'] >440000.0) & (df_inputs_prep['int_rate'] < 550000.0),1,0)
df_inputs_prep['annual_inc:550000.0-660000.0'] = np.where((df_inputs_prep['int_rate'] >550000.0) & (df_inputs_prep['int_rate'] < 660000.0),1,0)
In [141]:
df_inputs_prep_temp = df_inputs_prep[pd.notnull(df_inputs_prep['mths_since_last_delinq'])]
df_target_prep_temp = df_target_prep.loc[df_inputs_prep_temp.index]
df_inputs_prep_temp['mths_since_last_delinq_factor'] = pd.cut(df_inputs_prep_temp['mths_since_last_delinq'], 50)
df_temp = woe_ordered_continuous(df_inputs_prep_temp, 'mths_since_last_delinq_factor', df_target_prep_temp)
print(df_temp)
mths_since_last_delinq_factor n_obs prop_good prop_n_obs good_number \
0 (-0.179, 3.58] 12947 0.852939 0.030964 11043.0
1 (3.58, 7.16] 29369 0.862406 0.070238 25328.0
2 (7.16, 10.74] 22183 0.862913 0.053052 19142.0
3 (10.74, 14.32] 30337 0.863467 0.072553 26195.0
4 (14.32, 17.9] 21706 0.861835 0.051911 18707.0
5 (17.9, 21.48] 28156 0.872283 0.067337 24560.0
6 (21.48, 25.06] 27015 0.872145 0.064608 23561.0
7 (25.06, 28.64] 20324 0.867841 0.048606 17638.0
8 (28.64, 32.22] 25865 0.869708 0.061858 22495.0
9 (32.22, 35.8] 17868 0.875084 0.042733 15636.0
10 (35.8, 39.38] 22940 0.873714 0.054863 20043.0
11 (39.38, 42.96] 16785 0.877331 0.040142 14726.0
12 (42.96, 46.54] 21213 0.877434 0.050732 18613.0
13 (46.54, 50.12] 17817 0.878487 0.042611 15652.0
14 (50.12, 53.7] 10805 0.872466 0.025841 9427.0
15 (53.7, 57.28] 14462 0.871802 0.034587 12608.0
16 (57.28, 60.86] 10543 0.870435 0.025214 9177.0
17 (60.86, 64.44] 14436 0.868108 0.034525 12532.0
18 (64.44, 68.02] 14159 0.866092 0.033862 12263.0
19 (68.02, 71.6] 9802 0.865028 0.023442 8479.0
20 (71.6, 75.18] 12236 0.874551 0.029263 10701.0
21 (75.18, 78.76] 8138 0.875031 0.019463 7121.0
22 (78.76, 82.34] 7720 0.874482 0.018463 6751.0
23 (82.34, 85.92] 350 0.874286 0.000837 306.0
24 (85.92, 89.5] 233 0.866953 0.000557 202.0
25 (89.5, 93.08] 154 0.889610 0.000368 137.0
26 (93.08, 96.66] 96 0.916667 0.000230 88.0
27 (96.66, 100.24] 110 0.890909 0.000263 98.0
28 (100.24, 103.82] 71 0.901408 0.000170 64.0
29 (103.82, 107.4] 65 0.907692 0.000155 59.0
30 (107.4, 110.98] 49 0.918367 0.000117 45.0
31 (110.98, 114.56] 59 0.898305 0.000141 53.0
32 (114.56, 118.14] 32 0.875000 0.000077 28.0
33 (118.14, 121.72] 19 0.894737 0.000045 17.0
34 (121.72, 125.3] 13 0.846154 0.000031 11.0
35 (125.3, 128.88] 7 1.000000 0.000017 7.0
36 (128.88, 132.46] 8 1.000000 0.000019 8.0
37 (132.46, 136.04] 12 0.916667 0.000029 11.0
38 (136.04, 139.62] 3 1.000000 0.000007 3.0
39 (139.62, 143.2] 3 1.000000 0.000007 3.0
40 (143.2, 146.78] 5 1.000000 0.000012 5.0
41 (146.78, 150.36] 2 1.000000 0.000005 2.0
42 (150.36, 153.94] 1 0.000000 0.000002 0.0
43 (153.94, 157.52] 3 1.000000 0.000007 3.0
44 (157.52, 161.1] 9 0.777778 0.000022 7.0
45 (161.1, 164.68] 1 1.000000 0.000002 1.0
46 (164.68, 168.26] 0 NaN 0.000000 NaN
47 (168.26, 171.84] 3 0.666667 0.000007 2.0
48 (171.84, 175.42] 0 NaN 0.000000 NaN
49 (175.42, 179.0] 2 1.000000 0.000005 2.0
bad_number prop_good_bor prop_bad_bor WoE IV
0 1904.0 0.030375 0.034887 -0.138510 inf
1 4041.0 0.069667 0.074044 -0.060932 inf
2 3041.0 0.052652 0.055720 -0.056652 inf
3 4142.0 0.072051 0.075894 -0.051960 inf
4 2999.0 0.051455 0.054951 -0.065731 inf
5 3596.0 0.067554 0.065890 0.024947 inf
6 3454.0 0.064806 0.063288 0.023710 inf
7 2686.0 0.048515 0.049216 -0.014348 inf
8 3370.0 0.061874 0.061749 0.002030 inf
9 2232.0 0.043008 0.040897 0.050328 inf
10 2897.0 0.055130 0.053082 0.037854 inf
11 2059.0 0.040505 0.037727 0.071044 inf
12 2600.0 0.051197 0.047640 0.071999 inf
13 2165.0 0.043052 0.039669 0.081828 inf
14 1378.0 0.025930 0.025249 0.026595 inf
15 1854.0 0.034679 0.033971 0.020636 inf
16 1366.0 0.025242 0.025029 0.008463 inf
17 1904.0 0.034470 0.034887 -0.012022 inf
18 1896.0 0.033730 0.034741 -0.029510 inf
19 1323.0 0.023322 0.024241 -0.038659 inf
20 1535.0 0.029434 0.028126 0.045457 inf
21 1017.0 0.019587 0.018635 0.049841 inf
22 969.0 0.018569 0.017755 0.044831 inf
23 44.0 0.000842 0.000806 0.043045 inf
24 31.0 0.000556 0.000568 -0.022070 inf
25 17.0 0.000377 0.000311 0.190417 inf
26 8.0 0.000242 0.000147 0.501545 inf
27 12.0 0.000270 0.000220 0.203711 inf
28 7.0 0.000176 0.000128 0.316623 inf
29 6.0 0.000162 0.000110 0.389428 inf
30 4.0 0.000124 0.000073 0.524018 inf
31 6.0 0.000146 0.000110 0.282182 inf
32 4.0 0.000077 0.000073 0.049560 inf
33 2.0 0.000047 0.000037 0.243716 inf
34 2.0 0.000030 0.000037 -0.191602 inf
35 0.0 0.000019 0.000000 inf inf
36 0.0 0.000022 0.000000 inf inf
37 1.0 0.000030 0.000018 0.501545 inf
38 0.0 0.000008 0.000000 inf inf
39 0.0 0.000008 0.000000 inf inf
40 0.0 0.000014 0.000000 inf inf
41 0.0 0.000006 0.000000 inf inf
42 1.0 0.000000 0.000018 -inf inf
43 0.0 0.000008 0.000000 inf inf
44 2.0 0.000019 0.000037 -0.643587 inf
45 0.0 0.000003 0.000000 inf inf
46 NaN NaN NaN NaN inf
47 1.0 0.000006 0.000018 -1.203203 inf
48 NaN NaN NaN NaN inf
49 0.0 0.000006 0.000000 inf inf
C:\Users\computer\AppData\Local\Temp\ipykernel_3596\1994331492.py:3: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df_inputs_prep_temp['mths_since_last_delinq_factor'] = pd.cut(df_inputs_prep_temp['mths_since_last_delinq'], 50) C:\Users\computer\AppData\Local\Temp\ipykernel_3596\3982886971.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. df.groupby(df.columns.values[0], as_index=False)[df.columns.values[1]].count(), C:\Users\computer\AppData\Local\Temp\ipykernel_3596\3982886971.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. df.groupby(df.columns.values[0], as_index=False)[df.columns.values[1]].mean() C:\Users\computer\Music\python\Lib\site-packages\pandas\core\arraylike.py:399: RuntimeWarning: divide by zero encountered in log result = getattr(ufunc, method)(*inputs, **kwargs)
In [142]:
plot_by_woe(df_temp,90)
In [143]:
df_inputs_prep['mths_since_last_delinq:Missing'] = np.where((df_inputs_prep['mths_since_last_delinq'].isnull()), 1, 0)
df_inputs_prep['mths_since_last_deling:<17.9'] = np.where((df_inputs_prep['mths_since_last_delinq'] <= 17.9),1,0)
df_inputs_prep['mths_since_last_deling:17.9-75.18'] = np.where((df_inputs_prep['mths_since_last_delinq'] >17.9) & (df_inputs_prep['mths_since_last_delinq'] <75.18 ),1,0)
df_inputs_prep['mths_since_last_deling:75.18-100.24'] = np.where((df_inputs_prep['mths_since_last_delinq'] >75.18) & (df_inputs_prep['mths_since_last_delinq'] < 100.24),1,0)
df_inputs_prep['mths_since_last_deling:100.24-179'] = np.where((df_inputs_prep['mths_since_last_delinq'] >100.24) & (df_inputs_prep['mths_since_last_delinq'] < 179),1,0)
In [144]:
df_inputs_prep['mths_since_last_record_factor'] = pd.cut(df_inputs_prep['mths_since_last_record'],50)
In [145]:
df_temp = woe_ordered_continuous(df_inputs_prep, 'mths_since_last_record_factor', df_target_prep)
df_temp
C:\Users\computer\AppData\Local\Temp\ipykernel_3596\3982886971.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. df.groupby(df.columns.values[0], as_index=False)[df.columns.values[1]].count(), C:\Users\computer\AppData\Local\Temp\ipykernel_3596\3982886971.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. df.groupby(df.columns.values[0], as_index=False)[df.columns.values[1]].mean()
Out[145]:
| mths_since_last_record_factor | n_obs | prop_good | prop_n_obs | good_number | bad_number | prop_good_bor | prop_bad_bor | WoE | IV | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | (-0.124, 2.48] | 141 | 0.829787 | 0.001008 | 117.0 | 24.0 | 0.000989 | 0.001113 | -0.117437 | 0.012238 |
| 1 | (2.48, 4.96] | 295 | 0.820339 | 0.002110 | 242.0 | 53.0 | 0.002046 | 0.002457 | -0.182911 | 0.012238 |
| 2 | (4.96, 7.44] | 628 | 0.824841 | 0.004491 | 518.0 | 110.0 | 0.004380 | 0.005100 | -0.152062 | 0.012238 |
| 3 | (7.44, 9.92] | 544 | 0.860294 | 0.003891 | 468.0 | 76.0 | 0.003957 | 0.003523 | 0.116178 | 0.012238 |
| 4 | (9.92, 12.4] | 864 | 0.835648 | 0.006179 | 722.0 | 142.0 | 0.006105 | 0.006583 | -0.075359 | 0.012238 |
| 5 | (12.4, 14.88] | 615 | 0.845528 | 0.004398 | 520.0 | 95.0 | 0.004397 | 0.004404 | -0.001605 | 0.012238 |
| 6 | (14.88, 17.36] | 1025 | 0.853659 | 0.007330 | 875.0 | 150.0 | 0.007399 | 0.006954 | 0.062032 | 0.012238 |
| 7 | (17.36, 19.84] | 793 | 0.808323 | 0.005671 | 641.0 | 152.0 | 0.005420 | 0.007047 | -0.262408 | 0.012238 |
| 8 | (19.84, 22.32] | 1213 | 0.832646 | 0.008675 | 1010.0 | 203.0 | 0.008541 | 0.009411 | -0.097057 | 0.012238 |
| 9 | (22.32, 24.8] | 880 | 0.853409 | 0.006293 | 751.0 | 129.0 | 0.006351 | 0.005981 | 0.060036 | 0.012238 |
| 10 | (24.8, 27.28] | 1559 | 0.824888 | 0.011149 | 1286.0 | 273.0 | 0.010875 | 0.012656 | -0.151737 | 0.012238 |
| 11 | (27.28, 29.76] | 1183 | 0.848690 | 0.008460 | 1004.0 | 179.0 | 0.008490 | 0.008299 | 0.022805 | 0.012238 |
| 12 | (29.76, 32.24] | 2031 | 0.839980 | 0.014525 | 1706.0 | 325.0 | 0.014426 | 0.015067 | -0.043475 | 0.012238 |
| 13 | (32.24, 34.72] | 1509 | 0.828363 | 0.010792 | 1250.0 | 259.0 | 0.010570 | 0.012007 | -0.127486 | 0.012238 |
| 14 | (34.72, 37.2] | 2465 | 0.829615 | 0.017629 | 2045.0 | 420.0 | 0.017293 | 0.019471 | -0.118658 | 0.012238 |
| 15 | (37.2, 39.68] | 1857 | 0.824448 | 0.013281 | 1531.0 | 326.0 | 0.012946 | 0.015114 | -0.154778 | 0.012238 |
| 16 | (39.68, 42.16] | 2918 | 0.835504 | 0.020869 | 2438.0 | 480.0 | 0.020616 | 0.022253 | -0.076410 | 0.012238 |
| 17 | (42.16, 44.64] | 2092 | 0.834608 | 0.014961 | 1746.0 | 346.0 | 0.014764 | 0.016041 | -0.082913 | 0.012238 |
| 18 | (44.64, 47.12] | 3449 | 0.839664 | 0.024666 | 2896.0 | 553.0 | 0.024489 | 0.025637 | -0.045829 | 0.012238 |
| 19 | (47.12, 49.6] | 2480 | 0.834677 | 0.017736 | 2070.0 | 410.0 | 0.017504 | 0.019008 | -0.082410 | 0.012238 |
| 20 | (49.6, 52.08] | 4121 | 0.839359 | 0.029472 | 3459.0 | 662.0 | 0.029250 | 0.030691 | -0.048088 | 0.012238 |
| 21 | (52.08, 54.56] | 3193 | 0.839962 | 0.022835 | 2682.0 | 511.0 | 0.022679 | 0.023690 | -0.043608 | 0.012238 |
| 22 | (54.56, 57.04] | 5029 | 0.840724 | 0.035966 | 4228.0 | 801.0 | 0.035753 | 0.037135 | -0.037933 | 0.012238 |
| 23 | (57.04, 59.52] | 3562 | 0.833801 | 0.025474 | 2970.0 | 592.0 | 0.025115 | 0.027446 | -0.088746 | 0.012238 |
| 24 | (59.52, 62.0] | 5659 | 0.841138 | 0.040471 | 4760.0 | 899.0 | 0.040251 | 0.041678 | -0.034837 | 0.012238 |
| 25 | (62.0, 64.48] | 3898 | 0.835557 | 0.027877 | 3257.0 | 641.0 | 0.027542 | 0.029717 | -0.076024 | 0.012238 |
| 26 | (64.48, 66.96] | 3963 | 0.846329 | 0.028342 | 3354.0 | 609.0 | 0.028362 | 0.028234 | 0.004534 | 0.012238 |
| 27 | (66.96, 69.44] | 6324 | 0.843454 | 0.045227 | 5334.0 | 990.0 | 0.045105 | 0.045897 | -0.017405 | 0.012238 |
| 28 | (69.44, 71.92] | 4400 | 0.836591 | 0.031467 | 3681.0 | 719.0 | 0.031127 | 0.033333 | -0.068478 | 0.012238 |
| 29 | (71.92, 74.4] | 6386 | 0.833386 | 0.045671 | 5322.0 | 1064.0 | 0.045004 | 0.049328 | -0.091743 | 0.012238 |
| 30 | (74.4, 76.88] | 4434 | 0.851376 | 0.031711 | 3775.0 | 659.0 | 0.031922 | 0.030552 | 0.043875 | 0.012238 |
| 31 | (76.88, 79.36] | 6611 | 0.838602 | 0.047280 | 5544.0 | 1067.0 | 0.046881 | 0.049467 | -0.053691 | 0.012238 |
| 32 | (79.36, 81.84] | 4482 | 0.839581 | 0.032054 | 3763.0 | 719.0 | 0.031821 | 0.033333 | -0.046446 | 0.012238 |
| 33 | (81.84, 84.32] | 6195 | 0.845843 | 0.044305 | 5240.0 | 955.0 | 0.044310 | 0.044274 | 0.000809 | 0.012238 |
| 34 | (84.32, 86.8] | 3759 | 0.848098 | 0.026883 | 3188.0 | 571.0 | 0.026958 | 0.026472 | 0.018203 | 0.012238 |
| 35 | (86.8, 89.28] | 5111 | 0.847192 | 0.036552 | 4330.0 | 781.0 | 0.036615 | 0.036208 | 0.011191 | 0.012238 |
| 36 | (89.28, 91.76] | 3019 | 0.847632 | 0.021591 | 2559.0 | 460.0 | 0.021639 | 0.021326 | 0.014589 | 0.012238 |
| 37 | (91.76, 94.24] | 4439 | 0.855823 | 0.031746 | 3799.0 | 640.0 | 0.032125 | 0.029671 | 0.079468 | 0.012238 |
| 38 | (94.24, 96.72] | 2689 | 0.860171 | 0.019231 | 2313.0 | 376.0 | 0.019559 | 0.017432 | 0.115155 | 0.012238 |
| 39 | (96.72, 99.2] | 3944 | 0.874493 | 0.028206 | 3449.0 | 495.0 | 0.029165 | 0.022949 | 0.239725 | 0.012238 |
| 40 | (99.2, 101.68] | 2369 | 0.868299 | 0.016942 | 2057.0 | 312.0 | 0.017394 | 0.014465 | 0.184444 | 0.012238 |
| 41 | (101.68, 104.16] | 3289 | 0.865005 | 0.023522 | 2845.0 | 444.0 | 0.024058 | 0.020584 | 0.155937 | 0.012238 |
| 42 | (104.16, 106.64] | 2085 | 0.887290 | 0.014911 | 1850.0 | 235.0 | 0.015644 | 0.010895 | 0.361799 | 0.012238 |
| 43 | (106.64, 109.12] | 2847 | 0.884440 | 0.020361 | 2518.0 | 329.0 | 0.021293 | 0.015253 | 0.333606 | 0.012238 |
| 44 | (109.12, 111.6] | 1894 | 0.881204 | 0.013545 | 1669.0 | 225.0 | 0.014113 | 0.010431 | 0.302323 | 0.012238 |
| 45 | (111.6, 114.08] | 3087 | 0.859086 | 0.022077 | 2652.0 | 435.0 | 0.022426 | 0.020167 | 0.106167 | 0.012238 |
| 46 | (114.08, 116.56] | 2002 | 0.860140 | 0.014318 | 1722.0 | 280.0 | 0.014562 | 0.012981 | 0.114895 | 0.012238 |
| 47 | (116.56, 119.04] | 2463 | 0.840844 | 0.017615 | 2071.0 | 392.0 | 0.017513 | 0.018173 | -0.037032 | 0.012238 |
| 48 | (119.04, 121.52] | 17 | 0.941176 | 0.000122 | 16.0 | 1.0 | 0.000135 | 0.000046 | 1.071032 | 0.012238 |
| 49 | (121.52, 124.0] | 15 | 0.933333 | 0.000107 | 14.0 | 1.0 | 0.000118 | 0.000046 | 0.937501 | 0.012238 |
In [146]:
plot_by_woe(df_temp,90)
In [147]:
df_inputs_prep['mths_since_last_record_factor:<27.28'] = np.where((df_inputs_prep['mths_since_last_delinq'] <= 27.28),1,0)
df_inputs_prep['mths_since_last_record_factor:27.28-124.0'] = np.where((df_inputs_prep['mths_since_last_delinq'] >27.28) & (df_inputs_prep['mths_since_last_delinq'] < 124.0),1,0)
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
loan_data_inputs_train = df_inputs_prep
In [ ]:
In [218]:
#loan_data_inputs_train = df_inputs_prep
loan_data_inputs_test = df_inputs_prep
In [219]:
loan_data_inputs_train.to_csv(r"C:\Users\computer\Desktop\loan data inputs train.csv")
loan_data_target_train.to_csv(r"C:\Users\computer\Desktop\loan data target train.csv")
loan_data_inputs_test.to_csv(r"C:\Users\computer\Desktop\loan data inputs test.csv")
loan_data_target_test.to_csv(r"C:\Users\computer\Desktop\loan data target test.csv")
In [ ]:
In [ ]:
In [148]:
# Define your DataFrame (inputs_train_with_ref_cat) (replace this with your actual DataFrame)
inputs_train_with_ref_cat = loan_data_inputs_train.reindex(columns=[
'grade:A',
'grade:B',
'grade:C',
'grade:D',
'grade:E',
'grade:F',
'grade:G',
'home_ownership:MORTGAGE',
'home_ownership:OWN',
'home_ownership:RENT',
'addr_state:AL_AR',
'addr_state:OK_MS_SD_LA_NM_NV_HI',
'addr_state:NY_FL_IN_NE_NC_KY_MO_MD_OH_CA_PA_NJ_AZ_AK_MI_TN_MN_TX_VA_DE',
'addr_state:MA_UT_QI_GA_WY_RI_IL_ND_MT_KS_CT_CO_WV_WA_SC_DC_NH_OR_VT_ME',
'verification_status:Verified',
'verification_status:Source_Verified',
'verification_status:Not_Verified',
'purpose:small_business_moving_debt_consolidation',
'purpose:medical_house_other_renewable_energy_major_purchase',
'purpose:home_improvement_vacation_car_credit_card',
'term:36',
'term:60',
'initial_list_status:w',
'initial_list_status:f',
'emp_length:0',
'emp_length:1',
'emp_length:3',
'emp_length:4',
'emp_length:5-6',
'emp_length:7',
'emp_length:8',
'emp_length:9',
'emp_length:10',
'mth_earliest_cr_line_date_factor:111-134',
'mth_earliest_cr_line_date_factor:135-156',
'mth_earliest_cr_line_date_factor:157-414',
'mth_earliest_cr_line_date_factor:415-515',
'mth_earliest_cr_line_date_factor:516-671',
'int_rate:<16.609',
'int_rate:19.177-30.99',
'installment_factor:13-321',
'installment_factor:322-1310',
'installment_factor:1311-1719'
])
# Define your reference categories (ensure there are no missing commas)
ref_categories = [
'grade:G',
'home_ownership:RENT',
'addr_state:AL_AR',
'verification_status:Verified',
'purpose:small_business_moving_debt_consolidation',
'term:60',
'emp_length:0',
'initial_list_status:f',
'mth_earliest_cr_line_date_factor:111-134',
'int_rate:<16.609',
]
# Drop the reference categories from inputs_train_with_ref_cat
inputs_train = inputs_train_with_ref_cat.drop(ref_categories, axis=1)
# Show the first few rows of the modified dataframe
inputs_train.head()
Out[148]:
| grade:A | grade:B | grade:C | grade:D | grade:E | grade:F | home_ownership:MORTGAGE | home_ownership:OWN | addr_state:OK_MS_SD_LA_NM_NV_HI | addr_state:NY_FL_IN_NE_NC_KY_MO_MD_OH_CA_PA_NJ_AZ_AK_MI_TN_MN_TX_VA_DE | addr_state:MA_UT_QI_GA_WY_RI_IL_ND_MT_KS_CT_CO_WV_WA_SC_DC_NH_OR_VT_ME | verification_status:Source_Verified | verification_status:Not_Verified | purpose:medical_house_other_renewable_energy_major_purchase | purpose:home_improvement_vacation_car_credit_card | term:36 | initial_list_status:w | emp_length:1 | emp_length:3 | emp_length:4 | emp_length:5-6 | emp_length:7 | emp_length:8 | emp_length:9 | emp_length:10 | mth_earliest_cr_line_date_factor:135-156 | mth_earliest_cr_line_date_factor:157-414 | mth_earliest_cr_line_date_factor:415-515 | mth_earliest_cr_line_date_factor:516-671 | int_rate:19.177-30.99 | installment_factor:13-321 | installment_factor:322-1310 | installment_factor:1311-1719 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 408561 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 70143 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 708782 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
| 572694 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
| 774181 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
In [149]:
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
import pandas as pd
In [150]:
reg = LogisticRegression()
In [151]:
pd.options.display.max_rows = None
In [152]:
reg.fit(inputs_train,loan_data_target_train)
Out[152]:
LogisticRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression()
In [153]:
reg.coef_
Out[153]:
array([[ 3.0430621 , 2.21543605, 1.67767374, 1.19221981, 0.52504494,
0.030754 , 0.30670382, 0.15027793, -0.15407642, -0.05626637,
0.17112466, 0.10105427, 0.3377236 , 0.17081899, 0.03146343,
0.03262351, 0.18883575, 0.00899712, 0.06442839, 0.0747902 ,
0.09044283, 0.0333022 , -0.05148065, -0.00747485, 0.11659843,
0.38996766, -0.21638667, -0.17537985, -0.28857326, 0.46299583,
-0.11599586, -0.15476534, 0.22084991]])
In [154]:
feature_name = inputs_train.columns.values
In [155]:
summary_table = pd.DataFrame(columns = ['feature_name'], data = feature_name)
summary_table['Coefficients'] = np.transpose(reg.coef_)
summary_table.index = summary_table.index + 1
summary_table.loc[0] = ['Intercept', reg.intercept_[0]]
summary_table = summary_table.sort_index()
summary_table
Out[155]:
| feature_name | Coefficients | |
|---|---|---|
| 0 | Intercept | 0.002225 |
| 1 | grade:A | 3.043062 |
| 2 | grade:B | 2.215436 |
| 3 | grade:C | 1.677674 |
| 4 | grade:D | 1.192220 |
| 5 | grade:E | 0.525045 |
| 6 | grade:F | 0.030754 |
| 7 | home_ownership:MORTGAGE | 0.306704 |
| 8 | home_ownership:OWN | 0.150278 |
| 9 | addr_state:OK_MS_SD_LA_NM_NV_HI | -0.154076 |
| 10 | addr_state:NY_FL_IN_NE_NC_KY_MO_MD_OH_CA_PA_NJ... | -0.056266 |
| 11 | addr_state:MA_UT_QI_GA_WY_RI_IL_ND_MT_KS_CT_CO... | 0.171125 |
| 12 | verification_status:Source_Verified | 0.101054 |
| 13 | verification_status:Not_Verified | 0.337724 |
| 14 | purpose:medical_house_other_renewable_energy_m... | 0.170819 |
| 15 | purpose:home_improvement_vacation_car_credit_card | 0.031463 |
| 16 | term:36 | 0.032624 |
| 17 | initial_list_status:w | 0.188836 |
| 18 | emp_length:1 | 0.008997 |
| 19 | emp_length:3 | 0.064428 |
| 20 | emp_length:4 | 0.074790 |
| 21 | emp_length:5-6 | 0.090443 |
| 22 | emp_length:7 | 0.033302 |
| 23 | emp_length:8 | -0.051481 |
| 24 | emp_length:9 | -0.007475 |
| 25 | emp_length:10 | 0.116598 |
| 26 | mth_earliest_cr_line_date_factor:135-156 | 0.389968 |
| 27 | mth_earliest_cr_line_date_factor:157-414 | -0.216387 |
| 28 | mth_earliest_cr_line_date_factor:415-515 | -0.175380 |
| 29 | mth_earliest_cr_line_date_factor:516-671 | -0.288573 |
| 30 | int_rate:19.177-30.99 | 0.462996 |
| 31 | installment_factor:13-321 | -0.115996 |
| 32 | installment_factor:322-1310 | -0.154765 |
| 33 | installment_factor:1311-1719 | 0.220850 |
In [156]:
from sklearn import linear_model
import numpy as np
import scipy.stats as stat
import pandas as pd
class LogisticRegression_with_p_values:
def __init__(self, *args, **kwargs):
self.model = linear_model.LogisticRegression(*args, **kwargs)
def fit(self, X, y):
self.model.fit(X, y)
denom = (2.0 * (1.0 + np.cosh(self.model.decision_function(X))))
denom = np.tile(denom, (X.shape[1], 1)).T
F_ij = np.dot((X / denom).T, X)
# Add a small regularization term to the diagonal
F_ij += np.eye(F_ij.shape[0]) * 1e-4
Cramer_Rao = np.linalg.inv(F_ij)
sigma_estimates = np.sqrt(np.diagonal(Cramer_Rao))
z_scores = self.model.coef_[0] / sigma_estimates
p_values = [stat.norm.sf(abs(x)) * 2 for x in z_scores]
self.coef_ = self.model.coef_
self.intercept_ = self.model.intercept_
self.p_values = p_values
# Initialize and fit the custom logistic regression model
reg = LogisticRegression_with_p_values()
reg.fit(inputs_train, loan_data_target_train)
# Access the coefficients and p-values
feature_name = inputs_train.columns.values
summary_table = pd.DataFrame(columns=['feature_name'], data=feature_name)
summary_table['Coefficients'] = np.transpose(reg.coef_)
summary_table['p_values'] = ["{:.10f}".format(p) for p in reg.p_values] # Format p-values to full numbers
summary_table.index = summary_table.index + 1
summary_table.loc[0] = ['Intercept', reg.intercept_[0], None]
summary_table = summary_table.sort_index()
print(summary_table)
feature_name Coefficients \
0 Intercept 0.002225
1 grade:A 3.043062
2 grade:B 2.215436
3 grade:C 1.677674
4 grade:D 1.192220
5 grade:E 0.525045
6 grade:F 0.030754
7 home_ownership:MORTGAGE 0.306704
8 home_ownership:OWN 0.150278
9 addr_state:OK_MS_SD_LA_NM_NV_HI -0.154076
10 addr_state:NY_FL_IN_NE_NC_KY_MO_MD_OH_CA_PA_NJ... -0.056266
11 addr_state:MA_UT_QI_GA_WY_RI_IL_ND_MT_KS_CT_CO... 0.171125
12 verification_status:Source_Verified 0.101054
13 verification_status:Not_Verified 0.337724
14 purpose:medical_house_other_renewable_energy_m... 0.170819
15 purpose:home_improvement_vacation_car_credit_card 0.031463
16 term:36 0.032624
17 initial_list_status:w 0.188836
18 emp_length:1 0.008997
19 emp_length:3 0.064428
20 emp_length:4 0.074790
21 emp_length:5-6 0.090443
22 emp_length:7 0.033302
23 emp_length:8 -0.051481
24 emp_length:9 -0.007475
25 emp_length:10 0.116598
26 mth_earliest_cr_line_date_factor:135-156 0.389968
27 mth_earliest_cr_line_date_factor:157-414 -0.216387
28 mth_earliest_cr_line_date_factor:415-515 -0.175380
29 mth_earliest_cr_line_date_factor:516-671 -0.288573
30 int_rate:19.177-30.99 0.462996
31 installment_factor:13-321 -0.115996
32 installment_factor:322-1310 -0.154765
33 installment_factor:1311-1719 0.220850
p_values
0 None
1 0.0000000000
2 0.0000000000
3 0.0000000000
4 0.0000000000
5 0.0000000000
6 0.3712132778
7 0.0000000000
8 0.0000000000
9 0.0000000000
10 0.0009946438
11 0.0000000000
12 0.0000000000
13 0.0000000000
14 0.0000000000
15 0.0000828981
16 0.0000867422
17 0.0000000000
18 0.5413771634
19 0.0000036521
20 0.0000016788
21 0.0000000000
22 0.0716697137
23 0.0035439379
24 0.6945462563
25 0.0000000000
26 0.0000000000
27 0.0000000000
28 0.0000000000
29 0.0000000000
30 0.0000000000
31 0.0084331842
32 0.0003733566
33 0.0023352303
In [157]:
# Define your DataFrame (inputs_train_with_ref_cat) (replace this with your actual DataFrame)
inputs_train_with_ref_cat = loan_data_inputs_train.reindex(columns=[
'grade:A',
'grade:B',
'grade:C',
'grade:D',
'grade:E',
'grade:F',
'grade:G',
'home_ownership:MORTGAGE',
'home_ownership:OWN',
'home_ownership:RENT',
'addr_state:AL_AR',
'addr_state:OK_MS_SD_LA_NM_NV_HI',
'addr_state:NY_FL_IN_NE_NC_KY_MO_MD_OH_CA_PA_NJ_AZ_AK_MI_TN_MN_TX_VA_DE',
'addr_state:MA_UT_QI_GA_WY_RI_IL_ND_MT_KS_CT_CO_WV_WA_SC_DC_NH_OR_VT_ME',
'verification_status:Verified',
'verification_status:Source_Verified',
'verification_status:Not_Verified',
'purpose:small_business_moving_debt_consolidation',
'purpose:medical_house_other_renewable_energy_major_purchase',
'purpose:home_improvement_vacation_car_credit_card',
'term:36',
'term:60',
'initial_list_status:w',
'initial_list_status:f',
'emp_length:0',
'emp_length:1',
'emp_length:3',
'emp_length:4',
'emp_length:5-6',
'emp_length:7',
'emp_length:8',
'emp_length:9',
'emp_length:10',
'mth_earliest_cr_line_date_factor:111-134',
'mth_earliest_cr_line_date_factor:135-156',
'mth_earliest_cr_line_date_factor:157-414',
'mth_earliest_cr_line_date_factor:415-515',
'mth_earliest_cr_line_date_factor:516-671',
'int_rate:<16.609',
'int_rate:19.177-30.99',
'installment_factor:13-321',
'installment_factor:322-1310',
'installment_factor:1311-1719'
])
# Define your reference categories (ensure there are no missing commas)
ref_categories = [
'grade:G',
'home_ownership:RENT',
'addr_state:AL_AR',
'verification_status:Verified',
'purpose:small_business_moving_debt_consolidation',
'term:60',
'emp_length:0',
'initial_list_status:f',
'mth_earliest_cr_line_date_factor:111-134',
'int_rate:<16.609',
]
# Drop the reference categories from inputs_train_with_ref_cat
inputs_train = inputs_train_with_ref_cat.drop(ref_categories, axis=1)
# Show the first few rows of the modified dataframe
inputs_train.head()
Out[157]:
| grade:A | grade:B | grade:C | grade:D | grade:E | grade:F | home_ownership:MORTGAGE | home_ownership:OWN | addr_state:OK_MS_SD_LA_NM_NV_HI | addr_state:NY_FL_IN_NE_NC_KY_MO_MD_OH_CA_PA_NJ_AZ_AK_MI_TN_MN_TX_VA_DE | addr_state:MA_UT_QI_GA_WY_RI_IL_ND_MT_KS_CT_CO_WV_WA_SC_DC_NH_OR_VT_ME | verification_status:Source_Verified | verification_status:Not_Verified | purpose:medical_house_other_renewable_energy_major_purchase | purpose:home_improvement_vacation_car_credit_card | term:36 | initial_list_status:w | emp_length:1 | emp_length:3 | emp_length:4 | emp_length:5-6 | emp_length:7 | emp_length:8 | emp_length:9 | emp_length:10 | mth_earliest_cr_line_date_factor:135-156 | mth_earliest_cr_line_date_factor:157-414 | mth_earliest_cr_line_date_factor:415-515 | mth_earliest_cr_line_date_factor:516-671 | int_rate:19.177-30.99 | installment_factor:13-321 | installment_factor:322-1310 | installment_factor:1311-1719 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 408561 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 70143 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 708782 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
| 572694 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
| 774181 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
In [158]:
reg2 = LogisticRegression_with_p_values()
reg2.fit(inputs_train, loan_data_target_train)
In [159]:
import pickle
from sklearn.linear_model import LogisticRegression
# Assuming reg2 is your trained model (e.g., LogisticRegression)
reg2 = LogisticRegression() # or your custom model, e.g., LogisticRegression_with_p_values()
# Train the model (inputs_train and loan_data_target_train are your training data)
reg2.fit(inputs_train, loan_data_target_train)
# Save the trained model to a .sav file using pickle
with open('pd_model.sav', 'wb') as f:
pickle.dump(reg2, f)
print("Model saved successfully as pd_model.sav")
Model saved successfully as pd_model.sav
In [160]:
feature_name = inputs_train.columns.values
In [162]:
import pandas as pd
import numpy as np
import statsmodels.api as sm
# Assuming you have your data in `inputs_train` and `target_train`
# Add an intercept column (constant) to the feature set
inputs_train_with_intercept = sm.add_constant(inputs_train)
# Fit the logistic regression model using statsmodels
logit_model = sm.Logit(target_train, inputs_train_with_intercept)
logit_result = logit_model.fit()
# Extract coefficients and p-values
coefficients = logit_result.params
p_values = logit_result.pvalues
# Create the summary table
summary_table = pd.DataFrame({
'feature_name': coefficients.index,
'Coefficients': coefficients.values,
'p_values': p_values.values
})
# Add the intercept manually if needed
summary_table.loc[0] = ['Intercept', coefficients[0], p_values[0]]
# Format p-values to full precision
summary_table['p_values'] = summary_table['p_values'].apply(lambda x: "{:.10f}".format(x))
# Print the summary table
print(summary_table)
--------------------------------------------------------------------------- NameError Traceback (most recent call last) Cell In[162], line 10 7 inputs_train_with_intercept = sm.add_constant(inputs_train) 9 # Fit the logistic regression model using statsmodels ---> 10 logit_model = sm.Logit(target_train, inputs_train_with_intercept) 11 logit_result = logit_model.fit() 13 # Extract coefficients and p-values NameError: name 'target_train' is not defined
In [161]:
feature_name = inputs_train.columns.values
summary_table = pd.DataFrame(columns=['feature_name'], data=feature_name)
summary_table['Coefficients'] = np.transpose(reg2.coef_)
summary_table['p_values'] = ["{:.10f}".format(p) for p in reg2.p_values] # Format p-values to full numbers
summary_table.index = summary_table.index + 1
summary_table.loc[0] = ['Intercept', reg.intercept_[0], None]
summary_table = summary_table.sort_index()
print(summary_table)
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[161], line 4 2 summary_table = pd.DataFrame(columns=['feature_name'], data=feature_name) 3 summary_table['Coefficients'] = np.transpose(reg2.coef_) ----> 4 summary_table['p_values'] = ["{:.10f}".format(p) for p in reg2.p_values] # Format p-values to full numbers 5 summary_table.index = summary_table.index + 1 6 summary_table.loc[0] = ['Intercept', reg.intercept_[0], None] AttributeError: 'LogisticRegression' object has no attribute 'p_values'
In [ ]:
inputs_test_with_ref_cat = loan_data_inputs_test.reindex(columns=[
'grade:A',
'grade:B',
'grade:C',
'grade:D',
'grade:E',
'grade:F',
'grade:G',
'home_ownership:MORTGAGE_ANY',
'home_ownership:OWN',
'home_ownership:RENT',
'addr_state:AL_AR',
'addr_state:OK_MS_SD_LA_NM_NV_HI',
'addr_state:NY_FL_IN_NE_NC_KY_MO_MD_OH_CA_PA_NJ_AZ_AK_MI_TN_MN_TX_VA_DE',
'addr_state:MA_UT_QI_GA_WY_RI_IL_ND_MT_KS_CT_CO_WV_WA_SC_DC_NH_OR_VT_ME',
'verification_status:Verified',
'verification_status:Source_Verified',
'verification_status:Not_Verified',
'purpose:small_business_moving_debt_consolidation',
'purpose:medical_house_other_renewable_energy_major_purchase',
'purpose:home_improvement_vacation_car_credit_card',
'term:36',
'term:60',
'initial_list_status:w',
'initial_list_status:f',
'emp_length:0',
'emp_length:1',
'emp_length:3',
'emp_length:4',
'emp_length:5-6',
'emp_length:7',
'emp_length:8',
'emp_length:9',
'emp_length:10',
'mth_earliest_cr_line_date_factor:111-134',
'mth_earliest_cr_line_date_factor:135-156',
'mth_earliest_cr_line_date_factor:157-414',
'mth_earliest_cr_line_date_factor:415-515',
'mth_earliest_cr_line_date_factor:516-671',
'int_rate:<16.609',
'int_rate:19.177-30.99',
'installment_factor:13-321',
'installment_factor:322-1310',
'installment_factor:1311-1719'
])
# Define your reference categories (ensure there are no missing commas)
ref_categories = [
'grade:G',
'home_ownership:RENT',
'addr_state:AL_AR',
'verification_status:Verified',
'purpose:small_business_moving_debt_consolidation',
'term:60',
'emp_length:0',
'initial_list_status:f',
'mth_earliest_cr_line_date_factor:111-134',
'int_rate:<16.609',
]
# Drop the reference categories from inputs_train_with_ref_cat
inputs_test = inputs_test_with_ref_cat.drop(ref_categories, axis=1)
# Show the first few rows of the modified dataframe
inputs_test.head()
In [ ]:
y_hat_test = reg2.model.predict(inputs_test)
In [ ]:
y_hat_test
In [ ]:
y_hat_test_proba = reg2.model.predict_proba(inputs_test)
In [ ]:
y_hat_test_proba
In [ ]:
y_hat_test_proba[:][: , 1]
In [ ]:
y_hat_test_proba[:, 1]
In [ ]:
y_hat_test_proba = y_hat_test_proba[:, 1]
y_hat_test_proba
In [ ]:
loan_data_target_test_temp = loan_data_target_test
In [ ]:
loan_data_target_test_temp.reset_index(drop = True, inplace= True)
In [ ]:
df_actual_predicted_prob = pd.concat([loan_data_target_test_temp, pd.DataFrame(y_hat_test_proba)], axis = 1)
In [ ]:
df_actual_predicted_prob.shape
In [ ]:
df_actual_predicted_prob.columns = ['loan_data_target_test','y_hat_test_proba']
In [ ]:
df_actual_predicted_prob.index = loan_data_inputs_test.index
In [ ]:
df_actual_predicted_prob.head()
In [ ]:
# Check if there are any NaN values
print(df_actual_predicted_prob['loan_data_target_test'].isna().sum())
In [ ]:
df_actual_predicted_prob = df_actual_predicted_prob.dropna(subset=['loan_data_target_test'])
In [ ]:
df_actual_predicted_prob['loan_data_target_test'] = df_actual_predicted_prob['loan_data_target_test'].astype(int)
In [ ]:
df_actual_predicted_prob.head()
In [ ]:
pd.options.display.max_columns = None
In [ ]:
summary_table
In [ ]:
y_hat_test_proba
In [162]:
ref_categories
Out[162]:
['grade:G', 'home_ownership:RENT', 'addr_state:AL_AR', 'verification_status:Verified', 'purpose:small_business_moving_debt_consolidation', 'term:60', 'emp_length:0', 'initial_list_status:f', 'mth_earliest_cr_line_date_factor:111-134', 'int_rate:<16.609']
In [163]:
# Create the reference categories DataFrame
df_ref_categories = pd.DataFrame(ref_categories, columns=['feature_name'])
df_ref_categories['Coefficients'] = 0
df_ref_categories['p_values'] = np.nan
# Concatenate the summary table with the reference categories
df_scorecard = pd.concat([summary_table, df_ref_categories])
df_scorecard = df_scorecard.reset_index(drop=True)
# Add the reference categories at the end of the DataFrame
df_scorecard = pd.concat([summary_table, df_ref_categories], ignore_index=True)
# Drop the last column if it exists
if "Feature name" in df_scorecard.columns:
df_scorecard = df_scorecard.drop(columns=["Feature name"])
df_scorecard
Out[163]:
| feature_name | Coefficients | p_values | |
|---|---|---|---|
| 0 | Intercept | 0.116412 | None |
| 1 | grade:A | 3.005147 | 0.0000000000 |
| 2 | grade:B | 2.181705 | 0.0000000000 |
| 3 | grade:C | 1.651155 | 0.0000000000 |
| 4 | grade:D | 1.169382 | 0.0000000000 |
| 5 | grade:E | 0.518615 | 0.0000000000 |
| 6 | grade:F | 0.040875 | 0.2347057764 |
| 7 | home_ownership:MORTGAGE_ANY | 0.303130 | 0.0000000000 |
| 8 | home_ownership:OWN | 0.149419 | 0.0000000000 |
| 9 | addr_state:OK_MS_SD_LA_NM_NV_HI | -0.133763 | 0.0000000007 |
| 10 | addr_state:NY_FL_IN_NE_NC_KY_MO_MD_OH_CA_PA_NJ... | -0.041722 | 0.0141653326 |
| 11 | addr_state:MA_UT_QI_GA_WY_RI_IL_ND_MT_KS_CT_CO... | 0.184597 | 0.0000000000 |
| 12 | verification_status:Source_Verified | 0.102382 | 0.0000000000 |
| 13 | verification_status:Not_Verified | 0.337805 | 0.0000000000 |
| 14 | purpose:medical_house_other_renewable_energy_m... | 0.169085 | 0.0000000000 |
| 15 | purpose:home_improvement_vacation_car_credit_card | 0.036289 | 0.0000056405 |
| 16 | term:36 | 0.043852 | 0.0000001283 |
| 17 | initial_list_status:w | 0.194510 | 0.0000000000 |
| 18 | emp_length:1 | 0.013538 | 0.3581784849 |
| 19 | emp_length:3 | 0.066784 | 0.0000015743 |
| 20 | emp_length:4 | 0.083040 | 0.0000001089 |
| 21 | emp_length:5-6 | 0.092387 | 0.0000000000 |
| 22 | emp_length:7 | 0.027653 | 0.1339517516 |
| 23 | emp_length:8 | -0.055816 | 0.0015479674 |
| 24 | emp_length:9 | -0.012259 | 0.5189800904 |
| 25 | emp_length:10 | 0.113871 | 0.0000000000 |
| 26 | mth_earliest_cr_line_date_factor:135-156 | 0.045926 | 0.1615668807 |
| 27 | mth_earliest_cr_line_date_factor:157-414 | -0.324979 | 0.0000000000 |
| 28 | mth_earliest_cr_line_date_factor:415-515 | -0.246060 | 0.0000000000 |
| 29 | mth_earliest_cr_line_date_factor:516-671 | -0.427992 | 0.0000000000 |
| 30 | int_rate:19.177-30.99 | 0.445838 | 0.0000000000 |
| 31 | installment_factor:13-321 | -0.080997 | 0.0648951271 |
| 32 | installment_factor:322-1310 | -0.120031 | 0.0056403307 |
| 33 | installment_factor:1311-1719 | 0.231548 | 0.0013470072 |
| 34 | grade:G | 0.000000 | NaN |
| 35 | home_ownership:RENT | 0.000000 | NaN |
| 36 | addr_state:AL_AR | 0.000000 | NaN |
| 37 | verification_status:Verified | 0.000000 | NaN |
| 38 | purpose:small_business_moving_debt_consolidation | 0.000000 | NaN |
| 39 | term:60 | 0.000000 | NaN |
| 40 | emp_length:0 | 0.000000 | NaN |
| 41 | initial_list_status:f | 0.000000 | NaN |
| 42 | mth_earliest_cr_line_date_factor:111-134 | 0.000000 | NaN |
| 43 | int_rate:<16.609 | 0.000000 | NaN |
In [164]:
df_scorecard['Original_feature_name'] = df_scorecard['feature_name'].str.split(':').str[0]
df_scorecard
Out[164]:
| feature_name | Coefficients | p_values | Original_feature_name | |
|---|---|---|---|---|
| 0 | Intercept | 0.116412 | None | Intercept |
| 1 | grade:A | 3.005147 | 0.0000000000 | grade |
| 2 | grade:B | 2.181705 | 0.0000000000 | grade |
| 3 | grade:C | 1.651155 | 0.0000000000 | grade |
| 4 | grade:D | 1.169382 | 0.0000000000 | grade |
| 5 | grade:E | 0.518615 | 0.0000000000 | grade |
| 6 | grade:F | 0.040875 | 0.2347057764 | grade |
| 7 | home_ownership:MORTGAGE_ANY | 0.303130 | 0.0000000000 | home_ownership |
| 8 | home_ownership:OWN | 0.149419 | 0.0000000000 | home_ownership |
| 9 | addr_state:OK_MS_SD_LA_NM_NV_HI | -0.133763 | 0.0000000007 | addr_state |
| 10 | addr_state:NY_FL_IN_NE_NC_KY_MO_MD_OH_CA_PA_NJ... | -0.041722 | 0.0141653326 | addr_state |
| 11 | addr_state:MA_UT_QI_GA_WY_RI_IL_ND_MT_KS_CT_CO... | 0.184597 | 0.0000000000 | addr_state |
| 12 | verification_status:Source_Verified | 0.102382 | 0.0000000000 | verification_status |
| 13 | verification_status:Not_Verified | 0.337805 | 0.0000000000 | verification_status |
| 14 | purpose:medical_house_other_renewable_energy_m... | 0.169085 | 0.0000000000 | purpose |
| 15 | purpose:home_improvement_vacation_car_credit_card | 0.036289 | 0.0000056405 | purpose |
| 16 | term:36 | 0.043852 | 0.0000001283 | term |
| 17 | initial_list_status:w | 0.194510 | 0.0000000000 | initial_list_status |
| 18 | emp_length:1 | 0.013538 | 0.3581784849 | emp_length |
| 19 | emp_length:3 | 0.066784 | 0.0000015743 | emp_length |
| 20 | emp_length:4 | 0.083040 | 0.0000001089 | emp_length |
| 21 | emp_length:5-6 | 0.092387 | 0.0000000000 | emp_length |
| 22 | emp_length:7 | 0.027653 | 0.1339517516 | emp_length |
| 23 | emp_length:8 | -0.055816 | 0.0015479674 | emp_length |
| 24 | emp_length:9 | -0.012259 | 0.5189800904 | emp_length |
| 25 | emp_length:10 | 0.113871 | 0.0000000000 | emp_length |
| 26 | mth_earliest_cr_line_date_factor:135-156 | 0.045926 | 0.1615668807 | mth_earliest_cr_line_date_factor |
| 27 | mth_earliest_cr_line_date_factor:157-414 | -0.324979 | 0.0000000000 | mth_earliest_cr_line_date_factor |
| 28 | mth_earliest_cr_line_date_factor:415-515 | -0.246060 | 0.0000000000 | mth_earliest_cr_line_date_factor |
| 29 | mth_earliest_cr_line_date_factor:516-671 | -0.427992 | 0.0000000000 | mth_earliest_cr_line_date_factor |
| 30 | int_rate:19.177-30.99 | 0.445838 | 0.0000000000 | int_rate |
| 31 | installment_factor:13-321 | -0.080997 | 0.0648951271 | installment_factor |
| 32 | installment_factor:322-1310 | -0.120031 | 0.0056403307 | installment_factor |
| 33 | installment_factor:1311-1719 | 0.231548 | 0.0013470072 | installment_factor |
| 34 | grade:G | 0.000000 | NaN | grade |
| 35 | home_ownership:RENT | 0.000000 | NaN | home_ownership |
| 36 | addr_state:AL_AR | 0.000000 | NaN | addr_state |
| 37 | verification_status:Verified | 0.000000 | NaN | verification_status |
| 38 | purpose:small_business_moving_debt_consolidation | 0.000000 | NaN | purpose |
| 39 | term:60 | 0.000000 | NaN | term |
| 40 | emp_length:0 | 0.000000 | NaN | emp_length |
| 41 | initial_list_status:f | 0.000000 | NaN | initial_list_status |
| 42 | mth_earliest_cr_line_date_factor:111-134 | 0.000000 | NaN | mth_earliest_cr_line_date_factor |
| 43 | int_rate:<16.609 | 0.000000 | NaN | int_rate |
In [165]:
min_score = 300
max_score = 850
In [166]:
df_scorecard.groupby('Original_feature_name')['Coefficients'].min()
Out[166]:
Original_feature_name Intercept 0.116412 addr_state -0.133763 emp_length -0.055816 grade 0.000000 home_ownership 0.000000 initial_list_status 0.000000 installment_factor -0.120031 int_rate 0.000000 mth_earliest_cr_line_date_factor -0.427992 purpose 0.000000 term 0.000000 verification_status 0.000000 Name: Coefficients, dtype: float64
In [167]:
min_sum_coef = df_scorecard.groupby('Original_feature_name')['Coefficients'].min().sum()
min_sum_coef
Out[167]:
-0.6211915286694559
In [168]:
df_scorecard.groupby('Original_feature_name')['Coefficients'].max()
Out[168]:
Original_feature_name Intercept 0.116412 addr_state 0.184597 emp_length 0.113871 grade 3.005147 home_ownership 0.303130 initial_list_status 0.194510 installment_factor 0.231548 int_rate 0.445838 mth_earliest_cr_line_date_factor 0.045926 purpose 0.169085 term 0.043852 verification_status 0.337805 Name: Coefficients, dtype: float64
In [169]:
max_sum_coef = df_scorecard.groupby('Original_feature_name')['Coefficients'].max().sum()
max_sum_coef
Out[169]:
5.191719841746845
In [170]:
df_scorecard['Score - calculation'] = df_scorecard['Coefficients'] * (max_score - min_score) / (max_sum_coef - min_sum_coef)
df_scorecard
Out[170]:
| feature_name | Coefficients | p_values | Original_feature_name | Score - calculation | |
|---|---|---|---|---|---|
| 0 | Intercept | 0.116412 | None | Intercept | 11.014536 |
| 1 | grade:A | 3.005147 | 0.0000000000 | grade | 284.337851 |
| 2 | grade:B | 2.181705 | 0.0000000000 | grade | 206.426268 |
| 3 | grade:C | 1.651155 | 0.0000000000 | grade | 156.227241 |
| 4 | grade:D | 1.169382 | 0.0000000000 | grade | 110.643413 |
| 5 | grade:E | 0.518615 | 0.0000000000 | grade | 49.069745 |
| 6 | grade:F | 0.040875 | 0.2347057764 | grade | 3.867513 |
| 7 | home_ownership:MORTGAGE_ANY | 0.303130 | 0.0000000000 | home_ownership | 28.681200 |
| 8 | home_ownership:OWN | 0.149419 | 0.0000000000 | home_ownership | 14.137581 |
| 9 | addr_state:OK_MS_SD_LA_NM_NV_HI | -0.133763 | 0.0000000007 | addr_state | -12.656289 |
| 10 | addr_state:NY_FL_IN_NE_NC_KY_MO_MD_OH_CA_PA_NJ... | -0.041722 | 0.0141653326 | addr_state | -3.947567 |
| 11 | addr_state:MA_UT_QI_GA_WY_RI_IL_ND_MT_KS_CT_CO... | 0.184597 | 0.0000000000 | addr_state | 17.465966 |
| 12 | verification_status:Source_Verified | 0.102382 | 0.0000000000 | verification_status | 9.687072 |
| 13 | verification_status:Not_Verified | 0.337805 | 0.0000000000 | verification_status | 31.962123 |
| 14 | purpose:medical_house_other_renewable_energy_m... | 0.169085 | 0.0000000000 | purpose | 15.998275 |
| 15 | purpose:home_improvement_vacation_car_credit_card | 0.036289 | 0.0000056405 | purpose | 3.433576 |
| 16 | term:36 | 0.043852 | 0.0000001283 | term | 4.149175 |
| 17 | initial_list_status:w | 0.194510 | 0.0000000000 | initial_list_status | 18.403917 |
| 18 | emp_length:1 | 0.013538 | 0.3581784849 | emp_length | 1.280921 |
| 19 | emp_length:3 | 0.066784 | 0.0000015743 | emp_length | 6.318911 |
| 20 | emp_length:4 | 0.083040 | 0.0000001089 | emp_length | 7.856975 |
| 21 | emp_length:5-6 | 0.092387 | 0.0000000000 | emp_length | 8.741388 |
| 22 | emp_length:7 | 0.027653 | 0.1339517516 | emp_length | 2.616435 |
| 23 | emp_length:8 | -0.055816 | 0.0015479674 | emp_length | -5.281156 |
| 24 | emp_length:9 | -0.012259 | 0.5189800904 | emp_length | -1.159914 |
| 25 | emp_length:10 | 0.113871 | 0.0000000000 | emp_length | 10.774091 |
| 26 | mth_earliest_cr_line_date_factor:135-156 | 0.045926 | 0.1615668807 | mth_earliest_cr_line_date_factor | 4.345400 |
| 27 | mth_earliest_cr_line_date_factor:157-414 | -0.324979 | 0.0000000000 | mth_earliest_cr_line_date_factor | -30.748510 |
| 28 | mth_earliest_cr_line_date_factor:415-515 | -0.246060 | 0.0000000000 | mth_earliest_cr_line_date_factor | -23.281493 |
| 29 | mth_earliest_cr_line_date_factor:516-671 | -0.427992 | 0.0000000000 | mth_earliest_cr_line_date_factor | -40.495347 |
| 30 | int_rate:19.177-30.99 | 0.445838 | 0.0000000000 | int_rate | 42.183813 |
| 31 | installment_factor:13-321 | -0.080997 | 0.0648951271 | installment_factor | -7.663709 |
| 32 | installment_factor:322-1310 | -0.120031 | 0.0056403307 | installment_factor | -11.356998 |
| 33 | installment_factor:1311-1719 | 0.231548 | 0.0013470072 | installment_factor | 21.908400 |
| 34 | grade:G | 0.000000 | NaN | grade | 0.000000 |
| 35 | home_ownership:RENT | 0.000000 | NaN | home_ownership | 0.000000 |
| 36 | addr_state:AL_AR | 0.000000 | NaN | addr_state | 0.000000 |
| 37 | verification_status:Verified | 0.000000 | NaN | verification_status | 0.000000 |
| 38 | purpose:small_business_moving_debt_consolidation | 0.000000 | NaN | purpose | 0.000000 |
| 39 | term:60 | 0.000000 | NaN | term | 0.000000 |
| 40 | emp_length:0 | 0.000000 | NaN | emp_length | 0.000000 |
| 41 | initial_list_status:f | 0.000000 | NaN | initial_list_status | 0.000000 |
| 42 | mth_earliest_cr_line_date_factor:111-134 | 0.000000 | NaN | mth_earliest_cr_line_date_factor | 0.000000 |
| 43 | int_rate:<16.609 | 0.000000 | NaN | int_rate | 0.000000 |
In [171]:
df_scorecard['Score - calculation'][0] = (df_scorecard['Coefficients'][0] - min_sum_coef) / ((max_sum_coef - min_sum_coef)) *(max_score - min_score) + min_score
df_scorecard
C:\Users\computer\AppData\Local\Temp/ipykernel_19044/132595283.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df_scorecard['Score - calculation'][0] = (df_scorecard['Coefficients'][0] - min_sum_coef) / ((max_sum_coef - min_sum_coef)) *(max_score - min_score) + min_score
Out[171]:
| feature_name | Coefficients | p_values | Original_feature_name | Score - calculation | |
|---|---|---|---|---|---|
| 0 | Intercept | 0.116412 | None | Intercept | 369.789790 |
| 1 | grade:A | 3.005147 | 0.0000000000 | grade | 284.337851 |
| 2 | grade:B | 2.181705 | 0.0000000000 | grade | 206.426268 |
| 3 | grade:C | 1.651155 | 0.0000000000 | grade | 156.227241 |
| 4 | grade:D | 1.169382 | 0.0000000000 | grade | 110.643413 |
| 5 | grade:E | 0.518615 | 0.0000000000 | grade | 49.069745 |
| 6 | grade:F | 0.040875 | 0.2347057764 | grade | 3.867513 |
| 7 | home_ownership:MORTGAGE_ANY | 0.303130 | 0.0000000000 | home_ownership | 28.681200 |
| 8 | home_ownership:OWN | 0.149419 | 0.0000000000 | home_ownership | 14.137581 |
| 9 | addr_state:OK_MS_SD_LA_NM_NV_HI | -0.133763 | 0.0000000007 | addr_state | -12.656289 |
| 10 | addr_state:NY_FL_IN_NE_NC_KY_MO_MD_OH_CA_PA_NJ... | -0.041722 | 0.0141653326 | addr_state | -3.947567 |
| 11 | addr_state:MA_UT_QI_GA_WY_RI_IL_ND_MT_KS_CT_CO... | 0.184597 | 0.0000000000 | addr_state | 17.465966 |
| 12 | verification_status:Source_Verified | 0.102382 | 0.0000000000 | verification_status | 9.687072 |
| 13 | verification_status:Not_Verified | 0.337805 | 0.0000000000 | verification_status | 31.962123 |
| 14 | purpose:medical_house_other_renewable_energy_m... | 0.169085 | 0.0000000000 | purpose | 15.998275 |
| 15 | purpose:home_improvement_vacation_car_credit_card | 0.036289 | 0.0000056405 | purpose | 3.433576 |
| 16 | term:36 | 0.043852 | 0.0000001283 | term | 4.149175 |
| 17 | initial_list_status:w | 0.194510 | 0.0000000000 | initial_list_status | 18.403917 |
| 18 | emp_length:1 | 0.013538 | 0.3581784849 | emp_length | 1.280921 |
| 19 | emp_length:3 | 0.066784 | 0.0000015743 | emp_length | 6.318911 |
| 20 | emp_length:4 | 0.083040 | 0.0000001089 | emp_length | 7.856975 |
| 21 | emp_length:5-6 | 0.092387 | 0.0000000000 | emp_length | 8.741388 |
| 22 | emp_length:7 | 0.027653 | 0.1339517516 | emp_length | 2.616435 |
| 23 | emp_length:8 | -0.055816 | 0.0015479674 | emp_length | -5.281156 |
| 24 | emp_length:9 | -0.012259 | 0.5189800904 | emp_length | -1.159914 |
| 25 | emp_length:10 | 0.113871 | 0.0000000000 | emp_length | 10.774091 |
| 26 | mth_earliest_cr_line_date_factor:135-156 | 0.045926 | 0.1615668807 | mth_earliest_cr_line_date_factor | 4.345400 |
| 27 | mth_earliest_cr_line_date_factor:157-414 | -0.324979 | 0.0000000000 | mth_earliest_cr_line_date_factor | -30.748510 |
| 28 | mth_earliest_cr_line_date_factor:415-515 | -0.246060 | 0.0000000000 | mth_earliest_cr_line_date_factor | -23.281493 |
| 29 | mth_earliest_cr_line_date_factor:516-671 | -0.427992 | 0.0000000000 | mth_earliest_cr_line_date_factor | -40.495347 |
| 30 | int_rate:19.177-30.99 | 0.445838 | 0.0000000000 | int_rate | 42.183813 |
| 31 | installment_factor:13-321 | -0.080997 | 0.0648951271 | installment_factor | -7.663709 |
| 32 | installment_factor:322-1310 | -0.120031 | 0.0056403307 | installment_factor | -11.356998 |
| 33 | installment_factor:1311-1719 | 0.231548 | 0.0013470072 | installment_factor | 21.908400 |
| 34 | grade:G | 0.000000 | NaN | grade | 0.000000 |
| 35 | home_ownership:RENT | 0.000000 | NaN | home_ownership | 0.000000 |
| 36 | addr_state:AL_AR | 0.000000 | NaN | addr_state | 0.000000 |
| 37 | verification_status:Verified | 0.000000 | NaN | verification_status | 0.000000 |
| 38 | purpose:small_business_moving_debt_consolidation | 0.000000 | NaN | purpose | 0.000000 |
| 39 | term:60 | 0.000000 | NaN | term | 0.000000 |
| 40 | emp_length:0 | 0.000000 | NaN | emp_length | 0.000000 |
| 41 | initial_list_status:f | 0.000000 | NaN | initial_list_status | 0.000000 |
| 42 | mth_earliest_cr_line_date_factor:111-134 | 0.000000 | NaN | mth_earliest_cr_line_date_factor | 0.000000 |
| 43 | int_rate:<16.609 | 0.000000 | NaN | int_rate | 0.000000 |
In [172]:
df_scorecard['Score - Prelimenary'] = df_scorecard['Score - calculation'].round()
df_scorecard
Out[172]:
| feature_name | Coefficients | p_values | Original_feature_name | Score - calculation | Score - Prelimenary | |
|---|---|---|---|---|---|---|
| 0 | Intercept | 0.116412 | None | Intercept | 369.789790 | 370.0 |
| 1 | grade:A | 3.005147 | 0.0000000000 | grade | 284.337851 | 284.0 |
| 2 | grade:B | 2.181705 | 0.0000000000 | grade | 206.426268 | 206.0 |
| 3 | grade:C | 1.651155 | 0.0000000000 | grade | 156.227241 | 156.0 |
| 4 | grade:D | 1.169382 | 0.0000000000 | grade | 110.643413 | 111.0 |
| 5 | grade:E | 0.518615 | 0.0000000000 | grade | 49.069745 | 49.0 |
| 6 | grade:F | 0.040875 | 0.2347057764 | grade | 3.867513 | 4.0 |
| 7 | home_ownership:MORTGAGE_ANY | 0.303130 | 0.0000000000 | home_ownership | 28.681200 | 29.0 |
| 8 | home_ownership:OWN | 0.149419 | 0.0000000000 | home_ownership | 14.137581 | 14.0 |
| 9 | addr_state:OK_MS_SD_LA_NM_NV_HI | -0.133763 | 0.0000000007 | addr_state | -12.656289 | -13.0 |
| 10 | addr_state:NY_FL_IN_NE_NC_KY_MO_MD_OH_CA_PA_NJ... | -0.041722 | 0.0141653326 | addr_state | -3.947567 | -4.0 |
| 11 | addr_state:MA_UT_QI_GA_WY_RI_IL_ND_MT_KS_CT_CO... | 0.184597 | 0.0000000000 | addr_state | 17.465966 | 17.0 |
| 12 | verification_status:Source_Verified | 0.102382 | 0.0000000000 | verification_status | 9.687072 | 10.0 |
| 13 | verification_status:Not_Verified | 0.337805 | 0.0000000000 | verification_status | 31.962123 | 32.0 |
| 14 | purpose:medical_house_other_renewable_energy_m... | 0.169085 | 0.0000000000 | purpose | 15.998275 | 16.0 |
| 15 | purpose:home_improvement_vacation_car_credit_card | 0.036289 | 0.0000056405 | purpose | 3.433576 | 3.0 |
| 16 | term:36 | 0.043852 | 0.0000001283 | term | 4.149175 | 4.0 |
| 17 | initial_list_status:w | 0.194510 | 0.0000000000 | initial_list_status | 18.403917 | 18.0 |
| 18 | emp_length:1 | 0.013538 | 0.3581784849 | emp_length | 1.280921 | 1.0 |
| 19 | emp_length:3 | 0.066784 | 0.0000015743 | emp_length | 6.318911 | 6.0 |
| 20 | emp_length:4 | 0.083040 | 0.0000001089 | emp_length | 7.856975 | 8.0 |
| 21 | emp_length:5-6 | 0.092387 | 0.0000000000 | emp_length | 8.741388 | 9.0 |
| 22 | emp_length:7 | 0.027653 | 0.1339517516 | emp_length | 2.616435 | 3.0 |
| 23 | emp_length:8 | -0.055816 | 0.0015479674 | emp_length | -5.281156 | -5.0 |
| 24 | emp_length:9 | -0.012259 | 0.5189800904 | emp_length | -1.159914 | -1.0 |
| 25 | emp_length:10 | 0.113871 | 0.0000000000 | emp_length | 10.774091 | 11.0 |
| 26 | mth_earliest_cr_line_date_factor:135-156 | 0.045926 | 0.1615668807 | mth_earliest_cr_line_date_factor | 4.345400 | 4.0 |
| 27 | mth_earliest_cr_line_date_factor:157-414 | -0.324979 | 0.0000000000 | mth_earliest_cr_line_date_factor | -30.748510 | -31.0 |
| 28 | mth_earliest_cr_line_date_factor:415-515 | -0.246060 | 0.0000000000 | mth_earliest_cr_line_date_factor | -23.281493 | -23.0 |
| 29 | mth_earliest_cr_line_date_factor:516-671 | -0.427992 | 0.0000000000 | mth_earliest_cr_line_date_factor | -40.495347 | -40.0 |
| 30 | int_rate:19.177-30.99 | 0.445838 | 0.0000000000 | int_rate | 42.183813 | 42.0 |
| 31 | installment_factor:13-321 | -0.080997 | 0.0648951271 | installment_factor | -7.663709 | -8.0 |
| 32 | installment_factor:322-1310 | -0.120031 | 0.0056403307 | installment_factor | -11.356998 | -11.0 |
| 33 | installment_factor:1311-1719 | 0.231548 | 0.0013470072 | installment_factor | 21.908400 | 22.0 |
| 34 | grade:G | 0.000000 | NaN | grade | 0.000000 | 0.0 |
| 35 | home_ownership:RENT | 0.000000 | NaN | home_ownership | 0.000000 | 0.0 |
| 36 | addr_state:AL_AR | 0.000000 | NaN | addr_state | 0.000000 | 0.0 |
| 37 | verification_status:Verified | 0.000000 | NaN | verification_status | 0.000000 | 0.0 |
| 38 | purpose:small_business_moving_debt_consolidation | 0.000000 | NaN | purpose | 0.000000 | 0.0 |
| 39 | term:60 | 0.000000 | NaN | term | 0.000000 | 0.0 |
| 40 | emp_length:0 | 0.000000 | NaN | emp_length | 0.000000 | 0.0 |
| 41 | initial_list_status:f | 0.000000 | NaN | initial_list_status | 0.000000 | 0.0 |
| 42 | mth_earliest_cr_line_date_factor:111-134 | 0.000000 | NaN | mth_earliest_cr_line_date_factor | 0.000000 | 0.0 |
| 43 | int_rate:<16.609 | 0.000000 | NaN | int_rate | 0.000000 | 0.0 |
In [173]:
min_sum_score_prel = df_scorecard.groupby('Original_feature_name')['Score - Prelimenary'].min().sum()
min_sum_score_prel
Out[173]:
301.0
In [174]:
max_sum_score_prel = df_scorecard.groupby('Original_feature_name')['Score - Prelimenary'].max().sum()
max_sum_score_prel
Out[174]:
849.0
In [175]:
# Calculate current min and max sum scores for 'Score - Prelimenary'
current_min_sum_score_prel = df_scorecard.groupby('Original_feature_name')['Score - Prelimenary'].min().sum()
current_max_sum_score_prel = df_scorecard.groupby('Original_feature_name')['Score - Prelimenary'].max().sum()
print("Current min_sum_score_prel:", current_min_sum_score_prel)
print("Current max_sum_score_prel:", current_max_sum_score_prel)
# Calculate differences needed to reach the target scores
min_difference_prel = 300 - current_min_sum_score_prel
max_difference_prel = 850 - current_max_sum_score_prel
print("Difference needed for min_sum_score_prel:", min_difference_prel)
print("Difference needed for max_sum_score_prel:", max_difference_prel)
# Apply necessary adjustments to 'Score - Prelimenary'
df_scorecard.loc[0, 'Score - Prelimenary'] += min_difference_prel
df_scorecard.loc[1, 'Score - Prelimenary'] += max_difference_prel
# Recalculate min and max sum scores
adjusted_min_sum_score_prel = df_scorecard.groupby('Original_feature_name')['Score - Prelimenary'].min().sum()
adjusted_max_sum_score_prel = df_scorecard.groupby('Original_feature_name')['Score - Prelimenary'].max().sum()
print("Adjusted min_sum_score_prel:", adjusted_min_sum_score_prel)
print("Adjusted max_sum_score_prel:", adjusted_max_sum_score_prel)
# Copy adjusted 'Score - Prelimenary' to 'Score - Final'
df_scorecard['Score - Final'] = df_scorecard['Score - Prelimenary']
# Calculate current min and max sum scores for 'Score - Final'
current_min_sum_score_final = df_scorecard.groupby('Original_feature_name')['Score - Final'].min().sum()
current_max_sum_score_final = df_scorecard.groupby('Original_feature_name')['Score - Final'].max().sum()
print("Current min_sum_score_final:", current_min_sum_score_final)
print("Current max_sum_score_final:", current_max_sum_score_final)
# Calculate differences needed to reach the target scores for 'Score - Final'
min_difference_final = 300 - current_min_sum_score_final
max_difference_final = 850 - current_max_sum_score_final
print("Difference needed for min_sum_score_final:", min_difference_final)
print("Difference needed for max_sum_score_final:", max_difference_final)
# Apply necessary adjustments to 'Score - Final'
df_scorecard.loc[0, 'Score - Final'] += min_difference_final
# Ensure adjustments to meet both min and max targets, without overlapping indices
if min_difference_final == 0:
df_scorecard.loc[1, 'Score - Final'] += max_difference_final
else:
df_scorecard.loc[2, 'Score - Final'] += max_difference_final
# Recalculate min and max sum scores
adjusted_min_sum_score_final = df_scorecard.groupby('Original_feature_name')['Score - Final'].min().sum()
adjusted_max_sum_score_final = df_scorecard.groupby('Original_feature_name')['Score - Final'].max().sum()
print("Adjusted min_sum_score_final:", adjusted_min_sum_score_final)
print("Adjusted max_sum_score_final:", adjusted_max_sum_score_final)
Current min_sum_score_prel: 301.0 Current max_sum_score_prel: 849.0 Difference needed for min_sum_score_prel: -1.0 Difference needed for max_sum_score_prel: 1.0 Adjusted min_sum_score_prel: 300.0 Adjusted max_sum_score_prel: 849.0 Current min_sum_score_final: 300.0 Current max_sum_score_final: 849.0 Difference needed for min_sum_score_final: 0.0 Difference needed for max_sum_score_final: 1.0 Adjusted min_sum_score_final: 300.0 Adjusted max_sum_score_final: 850.0
In [176]:
min_sum_score_prel = df_scorecard.groupby('Original_feature_name')['Score - Final'].min().sum()
min_sum_score_prel
Out[176]:
300.0
In [177]:
max_sum_score_prel = df_scorecard.groupby('Original_feature_name')['Score - Final'].max().sum()
max_sum_score_prel
Out[177]:
850.0
In [178]:
inputs_test_with_ref_cat.head()
Out[178]:
| grade:A | grade:B | grade:C | grade:D | grade:E | grade:F | grade:G | home_ownership:MORTGAGE_ANY | home_ownership:OWN | home_ownership:RENT | addr_state:AL_AR | addr_state:OK_MS_SD_LA_NM_NV_HI | addr_state:NY_FL_IN_NE_NC_KY_MO_MD_OH_CA_PA_NJ_AZ_AK_MI_TN_MN_TX_VA_DE | addr_state:MA_UT_QI_GA_WY_RI_IL_ND_MT_KS_CT_CO_WV_WA_SC_DC_NH_OR_VT_ME | verification_status:Verified | verification_status:Source_Verified | verification_status:Not_Verified | purpose:small_business_moving_debt_consolidation | purpose:medical_house_other_renewable_energy_major_purchase | purpose:home_improvement_vacation_car_credit_card | term:36 | term:60 | initial_list_status:w | initial_list_status:f | emp_length:0 | emp_length:1 | emp_length:3 | emp_length:4 | emp_length:5-6 | emp_length:7 | emp_length:8 | emp_length:9 | emp_length:10 | mth_earliest_cr_line_date_factor:111-134 | mth_earliest_cr_line_date_factor:135-156 | mth_earliest_cr_line_date_factor:157-414 | mth_earliest_cr_line_date_factor:415-515 | mth_earliest_cr_line_date_factor:516-671 | int_rate:<16.609 | int_rate:19.177-30.99 | installment_factor:13-321 | installment_factor:322-1310 | installment_factor:1311-1719 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 408561 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 |
| 70143 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 708782 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| 572694 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| 774181 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
In [179]:
df_scorecard
Out[179]:
| feature_name | Coefficients | p_values | Original_feature_name | Score - calculation | Score - Prelimenary | Score - Final | |
|---|---|---|---|---|---|---|---|
| 0 | Intercept | 0.116412 | None | Intercept | 369.789790 | 369.0 | 369.0 |
| 1 | grade:A | 3.005147 | 0.0000000000 | grade | 284.337851 | 285.0 | 286.0 |
| 2 | grade:B | 2.181705 | 0.0000000000 | grade | 206.426268 | 206.0 | 206.0 |
| 3 | grade:C | 1.651155 | 0.0000000000 | grade | 156.227241 | 156.0 | 156.0 |
| 4 | grade:D | 1.169382 | 0.0000000000 | grade | 110.643413 | 111.0 | 111.0 |
| 5 | grade:E | 0.518615 | 0.0000000000 | grade | 49.069745 | 49.0 | 49.0 |
| 6 | grade:F | 0.040875 | 0.2347057764 | grade | 3.867513 | 4.0 | 4.0 |
| 7 | home_ownership:MORTGAGE_ANY | 0.303130 | 0.0000000000 | home_ownership | 28.681200 | 29.0 | 29.0 |
| 8 | home_ownership:OWN | 0.149419 | 0.0000000000 | home_ownership | 14.137581 | 14.0 | 14.0 |
| 9 | addr_state:OK_MS_SD_LA_NM_NV_HI | -0.133763 | 0.0000000007 | addr_state | -12.656289 | -13.0 | -13.0 |
| 10 | addr_state:NY_FL_IN_NE_NC_KY_MO_MD_OH_CA_PA_NJ... | -0.041722 | 0.0141653326 | addr_state | -3.947567 | -4.0 | -4.0 |
| 11 | addr_state:MA_UT_QI_GA_WY_RI_IL_ND_MT_KS_CT_CO... | 0.184597 | 0.0000000000 | addr_state | 17.465966 | 17.0 | 17.0 |
| 12 | verification_status:Source_Verified | 0.102382 | 0.0000000000 | verification_status | 9.687072 | 10.0 | 10.0 |
| 13 | verification_status:Not_Verified | 0.337805 | 0.0000000000 | verification_status | 31.962123 | 32.0 | 32.0 |
| 14 | purpose:medical_house_other_renewable_energy_m... | 0.169085 | 0.0000000000 | purpose | 15.998275 | 16.0 | 16.0 |
| 15 | purpose:home_improvement_vacation_car_credit_card | 0.036289 | 0.0000056405 | purpose | 3.433576 | 3.0 | 3.0 |
| 16 | term:36 | 0.043852 | 0.0000001283 | term | 4.149175 | 4.0 | 4.0 |
| 17 | initial_list_status:w | 0.194510 | 0.0000000000 | initial_list_status | 18.403917 | 18.0 | 18.0 |
| 18 | emp_length:1 | 0.013538 | 0.3581784849 | emp_length | 1.280921 | 1.0 | 1.0 |
| 19 | emp_length:3 | 0.066784 | 0.0000015743 | emp_length | 6.318911 | 6.0 | 6.0 |
| 20 | emp_length:4 | 0.083040 | 0.0000001089 | emp_length | 7.856975 | 8.0 | 8.0 |
| 21 | emp_length:5-6 | 0.092387 | 0.0000000000 | emp_length | 8.741388 | 9.0 | 9.0 |
| 22 | emp_length:7 | 0.027653 | 0.1339517516 | emp_length | 2.616435 | 3.0 | 3.0 |
| 23 | emp_length:8 | -0.055816 | 0.0015479674 | emp_length | -5.281156 | -5.0 | -5.0 |
| 24 | emp_length:9 | -0.012259 | 0.5189800904 | emp_length | -1.159914 | -1.0 | -1.0 |
| 25 | emp_length:10 | 0.113871 | 0.0000000000 | emp_length | 10.774091 | 11.0 | 11.0 |
| 26 | mth_earliest_cr_line_date_factor:135-156 | 0.045926 | 0.1615668807 | mth_earliest_cr_line_date_factor | 4.345400 | 4.0 | 4.0 |
| 27 | mth_earliest_cr_line_date_factor:157-414 | -0.324979 | 0.0000000000 | mth_earliest_cr_line_date_factor | -30.748510 | -31.0 | -31.0 |
| 28 | mth_earliest_cr_line_date_factor:415-515 | -0.246060 | 0.0000000000 | mth_earliest_cr_line_date_factor | -23.281493 | -23.0 | -23.0 |
| 29 | mth_earliest_cr_line_date_factor:516-671 | -0.427992 | 0.0000000000 | mth_earliest_cr_line_date_factor | -40.495347 | -40.0 | -40.0 |
| 30 | int_rate:19.177-30.99 | 0.445838 | 0.0000000000 | int_rate | 42.183813 | 42.0 | 42.0 |
| 31 | installment_factor:13-321 | -0.080997 | 0.0648951271 | installment_factor | -7.663709 | -8.0 | -8.0 |
| 32 | installment_factor:322-1310 | -0.120031 | 0.0056403307 | installment_factor | -11.356998 | -11.0 | -11.0 |
| 33 | installment_factor:1311-1719 | 0.231548 | 0.0013470072 | installment_factor | 21.908400 | 22.0 | 22.0 |
| 34 | grade:G | 0.000000 | NaN | grade | 0.000000 | 0.0 | 0.0 |
| 35 | home_ownership:RENT | 0.000000 | NaN | home_ownership | 0.000000 | 0.0 | 0.0 |
| 36 | addr_state:AL_AR | 0.000000 | NaN | addr_state | 0.000000 | 0.0 | 0.0 |
| 37 | verification_status:Verified | 0.000000 | NaN | verification_status | 0.000000 | 0.0 | 0.0 |
| 38 | purpose:small_business_moving_debt_consolidation | 0.000000 | NaN | purpose | 0.000000 | 0.0 | 0.0 |
| 39 | term:60 | 0.000000 | NaN | term | 0.000000 | 0.0 | 0.0 |
| 40 | emp_length:0 | 0.000000 | NaN | emp_length | 0.000000 | 0.0 | 0.0 |
| 41 | initial_list_status:f | 0.000000 | NaN | initial_list_status | 0.000000 | 0.0 | 0.0 |
| 42 | mth_earliest_cr_line_date_factor:111-134 | 0.000000 | NaN | mth_earliest_cr_line_date_factor | 0.000000 | 0.0 | 0.0 |
| 43 | int_rate:<16.609 | 0.000000 | NaN | int_rate | 0.000000 | 0.0 | 0.0 |
In [180]:
inputs_test_with_ref_cat_w_intercept = inputs_test_with_ref_cat
In [181]:
inputs_test_with_ref_cat_w_intercept.insert(0, 'Intercept', 1)
In [182]:
inputs_test_with_ref_cat_w_intercept.head()
Out[182]:
| Intercept | grade:A | grade:B | grade:C | grade:D | grade:E | grade:F | grade:G | home_ownership:MORTGAGE_ANY | home_ownership:OWN | home_ownership:RENT | addr_state:AL_AR | addr_state:OK_MS_SD_LA_NM_NV_HI | addr_state:NY_FL_IN_NE_NC_KY_MO_MD_OH_CA_PA_NJ_AZ_AK_MI_TN_MN_TX_VA_DE | addr_state:MA_UT_QI_GA_WY_RI_IL_ND_MT_KS_CT_CO_WV_WA_SC_DC_NH_OR_VT_ME | verification_status:Verified | verification_status:Source_Verified | verification_status:Not_Verified | purpose:small_business_moving_debt_consolidation | purpose:medical_house_other_renewable_energy_major_purchase | purpose:home_improvement_vacation_car_credit_card | term:36 | term:60 | initial_list_status:w | initial_list_status:f | emp_length:0 | emp_length:1 | emp_length:3 | emp_length:4 | emp_length:5-6 | emp_length:7 | emp_length:8 | emp_length:9 | emp_length:10 | mth_earliest_cr_line_date_factor:111-134 | mth_earliest_cr_line_date_factor:135-156 | mth_earliest_cr_line_date_factor:157-414 | mth_earliest_cr_line_date_factor:415-515 | mth_earliest_cr_line_date_factor:516-671 | int_rate:<16.609 | int_rate:19.177-30.99 | installment_factor:13-321 | installment_factor:322-1310 | installment_factor:1311-1719 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 408561 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 |
| 70143 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 708782 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| 572694 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| 774181 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
In [183]:
inputs_test_with_ref_cat_w_intercept = inputs_test_with_ref_cat_w_intercept[df_scorecard['feature_name'].values]
In [184]:
inputs_test_with_ref_cat_w_intercept.head()
Out[184]:
| Intercept | grade:A | grade:B | grade:C | grade:D | grade:E | grade:F | home_ownership:MORTGAGE_ANY | home_ownership:OWN | addr_state:OK_MS_SD_LA_NM_NV_HI | addr_state:NY_FL_IN_NE_NC_KY_MO_MD_OH_CA_PA_NJ_AZ_AK_MI_TN_MN_TX_VA_DE | addr_state:MA_UT_QI_GA_WY_RI_IL_ND_MT_KS_CT_CO_WV_WA_SC_DC_NH_OR_VT_ME | verification_status:Source_Verified | verification_status:Not_Verified | purpose:medical_house_other_renewable_energy_major_purchase | purpose:home_improvement_vacation_car_credit_card | term:36 | initial_list_status:w | emp_length:1 | emp_length:3 | emp_length:4 | emp_length:5-6 | emp_length:7 | emp_length:8 | emp_length:9 | emp_length:10 | mth_earliest_cr_line_date_factor:135-156 | mth_earliest_cr_line_date_factor:157-414 | mth_earliest_cr_line_date_factor:415-515 | mth_earliest_cr_line_date_factor:516-671 | int_rate:19.177-30.99 | installment_factor:13-321 | installment_factor:322-1310 | installment_factor:1311-1719 | grade:G | home_ownership:RENT | addr_state:AL_AR | verification_status:Verified | purpose:small_business_moving_debt_consolidation | term:60 | emp_length:0 | initial_list_status:f | mth_earliest_cr_line_date_factor:111-134 | int_rate:<16.609 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 408561 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 70143 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 |
| 708782 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 572694 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| 774181 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
In [185]:
scorecard_score = df_scorecard['Score - Final']
In [186]:
inputs_test_with_ref_cat_w_intercept.shape
Out[186]:
(838860, 44)
In [187]:
scorecard_score.shape
Out[187]:
(44,)
In [188]:
scorecard_score = scorecard_score.values.reshape(44,1)
In [189]:
scorecard_score.shape
Out[189]:
(44, 1)
In [190]:
y_scores = inputs_test_with_ref_cat_w_intercept.dot(scorecard_score)
In [191]:
y_scores.head()
Out[191]:
| 0 | |
|---|---|
| 408561 | 682.0 |
| 70143 | 513.0 |
| 708782 | 584.0 |
| 572694 | 542.0 |
| 774181 | 536.0 |
In [192]:
sum_coef_from_score = ((y_scores - min_score) / (max_score - min_score)) * (max_sum_coef - min_sum_coef) + min_sum_coef
In [193]:
y_hat_proba_from_score = np.exp(sum_coef_from_score) / (np.exp(sum_coef_from_score) + 1)
y_hat_proba_from_score.head()
Out[193]:
| 0 | |
|---|---|
| 408561 | 0.968205 |
| 70143 | 0.836168 |
| 708782 | 0.915319 |
| 572694 | 0.873966 |
| 774181 | 0.866814 |
In [194]:
y_hat_test_proba[0:5]
Out[194]:
array([0.9668202 , 0.83223981, 0.91444976, 0.87297412, 0.86543399])
In [195]:
tr = 0.5
df_actual_predicted_prob['y_hat_test'] = np.where(df_actual_predicted_prob['y_hat_test_proba'] > tr, 1, 0)
In [196]:
pd.crosstab(df_actual_predicted_prob['loan_data_target_test'], df_actual_predicted_prob['y_hat_test'],
rownames = ['Actual'], colnames = ['Predicted'])
Out[196]:
| Predicted | 0 | 1 |
|---|---|---|
| Actual | ||
| 0 | 6 | 25994 |
| 1 | 44 | 183671 |
In [197]:
pd.crosstab(df_actual_predicted_prob['loan_data_target_test'], df_actual_predicted_prob['y_hat_test'],
rownames = ['Actual'], colnames = ['Predicted']) / df_actual_predicted_prob.shape[0]
Out[197]:
| Predicted | 0 | 1 |
|---|---|---|
| Actual | ||
| 0 | 0.000029 | 0.123949 |
| 1 | 0.000210 | 0.875812 |
In [198]:
(pd.crosstab(df_actual_predicted_prob['loan_data_target_test'], df_actual_predicted_prob['y_hat_test'],
rownames = ['Actual'], colnames = ['Predicted']) / df_actual_predicted_prob.shape[0]).iloc[0,0] +(pd.crosstab(df_actual_predicted_prob['loan_data_target_test'], df_actual_predicted_prob['y_hat_test'],
rownames = ['Actual'], colnames = ['Predicted']) / df_actual_predicted_prob.shape[0]).iloc[1,1]
Out[198]:
0.8758410223398422
In [199]:
from sklearn.metrics import roc_curve, roc_auc_score
In [200]:
roc_curve(df_actual_predicted_prob['loan_data_target_test'], df_actual_predicted_prob['y_hat_test'])
Out[200]:
(array([0. , 0.99976923, 1. ]), array([0. , 0.9997605, 1. ]), array([inf, 1., 0.]))
In [201]:
fpr, tpr, thresholds = roc_curve(df_actual_predicted_prob['loan_data_target_test'],
df_actual_predicted_prob['y_hat_test_proba'])
In [202]:
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
In [203]:
plt.plot(fpr, tpr)
plt.plot(fpr, tpr, linestyle = '--', color = 'k')
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.title('ROC curve')
Out[203]:
Text(0.5, 1.0, 'ROC curve')
In [204]:
thresholds
Out[204]:
array([ inf, 0.98758733, 0.98571849, ..., 0.44118025, 0.43833984,
0.39709377])
In [205]:
#remove inf
thresholds_cleaned = thresholds[np.isfinite(thresholds)]
thresholds_cleaned
Out[205]:
array([0.98758733, 0.98571849, 0.98493609, ..., 0.44118025, 0.43833984,
0.39709377])
In [206]:
thresholds_cleaned.shape
Out[206]:
(30127,)
In [207]:
df_cutoffs = pd.concat([pd.DataFrame(thresholds_cleaned),pd.DataFrame(fpr), pd.DataFrame(tpr)], axis = 1 )
In [208]:
df_cutoffs.head()
Out[208]:
| 0 | 0 | 0 | |
|---|---|---|---|
| 0 | 0.987587 | 0.000000 | 0.000000 |
| 1 | 0.985718 | 0.000000 | 0.000011 |
| 2 | 0.984936 | 0.000000 | 0.000033 |
| 3 | 0.984501 | 0.000000 | 0.000054 |
| 4 | 0.984088 | 0.000038 | 0.000060 |
In [209]:
df_cutoffs.columns = ['thresholds_cleaned', 'fpr', 'tpr']
df_cutoffs.head()
Out[209]:
| thresholds_cleaned | fpr | tpr | |
|---|---|---|---|
| 0 | 0.987587 | 0.000000 | 0.000000 |
| 1 | 0.985718 | 0.000000 | 0.000011 |
| 2 | 0.984936 | 0.000000 | 0.000033 |
| 3 | 0.984501 | 0.000000 | 0.000054 |
| 4 | 0.984088 | 0.000038 | 0.000060 |
In [210]:
df_cutoffs['Score'] = ((np.log(df_cutoffs['thresholds_cleaned'] / (1 - df_cutoffs['thresholds_cleaned'])) - min_sum_coef) * ((max_score - min_score) / (max_sum_coef - min_sum_coef)) + min_score).round()
In [211]:
df_cutoffs.head()
Out[211]:
| thresholds_cleaned | fpr | tpr | Score | |
|---|---|---|---|---|
| 0 | 0.987587 | 0.000000 | 0.000000 | 773.0 |
| 1 | 0.985718 | 0.000000 | 0.000011 | 759.0 |
| 2 | 0.984936 | 0.000000 | 0.000033 | 754.0 |
| 3 | 0.984501 | 0.000000 | 0.000054 | 752.0 |
| 4 | 0.984088 | 0.000038 | 0.000060 | 749.0 |
In [212]:
df_cutoffs['Score'][0] = max_score
In [213]:
df_cutoffs.head()
Out[213]:
| thresholds_cleaned | fpr | tpr | Score | |
|---|---|---|---|---|
| 0 | 0.987587 | 0.000000 | 0.000000 | 850.0 |
| 1 | 0.985718 | 0.000000 | 0.000011 | 759.0 |
| 2 | 0.984936 | 0.000000 | 0.000033 | 754.0 |
| 3 | 0.984501 | 0.000000 | 0.000054 | 752.0 |
| 4 | 0.984088 | 0.000038 | 0.000060 | 749.0 |
In [214]:
def n_approved(p):
return np.where(df_actual_predicted_prob['y_hat_test_proba'] >= p, 1, 0).sum()
In [215]:
df_cutoffs['N Approved'] =df_cutoffs['thresholds_cleaned'].apply(n_approved)
df_cutoffs['N Rejected'] = df_actual_predicted_prob['y_hat_test_proba'].shape[0] - df_cutoffs['thresholds_cleaned'].apply(n_approved)
df_cutoffs['Approval Rate'] = df_cutoffs['N Approved'] / df_actual_predicted_prob['y_hat_test_proba'].shape[0]
df_cutoffs['Rejection Rate'] = 1 - df_cutoffs['Approval Rate']
In [216]:
df_cutoffs.head()
Out[216]:
| thresholds_cleaned | fpr | tpr | Score | N Approved | N Rejected | Approval Rate | Rejection Rate | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.987587 | 0.000000 | 0.000000 | 850.0 | 2 | 209713 | 0.000010 | 0.999990 |
| 1 | 0.985718 | 0.000000 | 0.000011 | 759.0 | 6 | 209709 | 0.000029 | 0.999971 |
| 2 | 0.984936 | 0.000000 | 0.000033 | 754.0 | 10 | 209705 | 0.000048 | 0.999952 |
| 3 | 0.984501 | 0.000000 | 0.000054 | 752.0 | 12 | 209703 | 0.000057 | 0.999943 |
| 4 | 0.984088 | 0.000038 | 0.000060 | 749.0 | 14 | 209701 | 0.000067 | 0.999933 |
In [217]:
df_cutoffs.tail()
Out[217]:
| thresholds_cleaned | fpr | tpr | Score | N Approved | N Rejected | Approval Rate | Rejection Rate | |
|---|---|---|---|---|---|---|---|---|
| 30123 | 0.458281 | 0.999923 | 0.999924 | 343.0 | 209701 | 14 | 0.999933 | 0.000067 |
| 30124 | 0.441180 | 1.000000 | 0.999924 | 336.0 | 209707 | 8 | 0.999962 | 0.000038 |
| 30125 | 0.438340 | 1.000000 | 0.999956 | 335.0 | 209709 | 6 | 0.999971 | 0.000029 |
| 30126 | 0.397094 | 1.000000 | 0.999967 | 319.0 | 209715 | 0 | 1.000000 | 0.000000 |
| 30127 | NaN | 1.000000 | 1.000000 | NaN | 0 | 209715 | 0.000000 | 1.000000 |
In [10]:
import pandas as pd
import numpy as np
loan_data = pd.read_csv(r"C:\Users\computer\Desktop\vvvv.csv")
C:\Users\computer\AppData\Local\Temp/ipykernel_2444/509372657.py:3: DtypeWarning: Columns (0,19,59,118,129,130,131,134,135,136,139,145,146,147) have mixed types. Specify dtype option on import or set low_memory=False. loan_data = pd.read_csv(r"C:\Users\computer\Desktop\vvvv.csv")
In [11]:
loan_data_defaults = loan_data[loan_data['loan_status'].isin(['Charged Off', 'Does not meet the credit policy:Status Charged Off'])]
In [12]:
loan_data_defaults.shape
Out[12]:
(129187, 279)
In [13]:
pd.options.display.max_rows = None
In [14]:
loan_data_defaults.isnull().sum()
Out[14]:
id 0 member_id 129187 loan_amnt 0 funded_amnt 0 funded_amnt_inv 0 term 0 int_rate 0 installment 0 grade 0 sub_grade 0 emp_title 10713 emp_length 10642 home_ownership 0 annual_inc 0 verification_status 0 issue_d 0 loan_status 0 pymnt_plan 0 url 0 desc 129182 purpose 0 title 2606 zip_code 0 addr_state 0 dti 37 delinq_2yrs 0 earliest_cr_line 0 fico_range_low 0 fico_range_high 0 inq_last_6mths 0 mths_since_last_delinq 60893 mths_since_last_record 102165 open_acc 0 pub_rec 0 revol_bal 0 revol_util 67 total_acc 0 initial_list_status 0 out_prncp 0 out_prncp_inv 0 total_pymnt 0 total_pymnt_inv 0 total_rec_prncp 0 total_rec_int 0 total_rec_late_fee 0 recoveries 0 collection_recovery_fee 0 last_pymnt_d 1096 last_pymnt_amnt 0 next_pymnt_d 129187 last_credit_pull_d 1 last_fico_range_high 0 last_fico_range_low 0 collections_12_mths_ex_med 0 mths_since_last_major_derog 88818 policy_code 0 application_type 0 annual_inc_joint 125837 dti_joint 125837 verification_status_joint 125845 acc_now_delinq 0 tot_coll_amt 0 tot_cur_bal 0 open_acc_6m 71950 open_act_il 71950 open_il_12m 71950 open_il_24m 71950 mths_since_rcnt_il 73755 total_bal_il 71950 il_util 79241 open_rv_12m 71950 open_rv_24m 71950 max_bal_bc 71950 all_util 71954 total_rev_hi_lim 0 inq_fi 71950 total_cu_tl 71950 inq_last_12m 71950 acc_open_past_24mths 0 avg_cur_bal 2 bc_open_to_buy 1454 bc_util 1530 chargeoff_within_12_mths 0 delinq_amnt 0 mo_sin_old_il_acct 3876 mo_sin_old_rev_tl_op 0 mo_sin_rcnt_rev_tl_op 0 mo_sin_rcnt_tl 0 mort_acc 0 mths_since_recent_bc 1375 mths_since_recent_bc_dlq 95168 mths_since_recent_inq 9103 mths_since_recent_revol_delinq 82063 num_accts_ever_120_pd 0 num_actv_bc_tl 0 num_actv_rev_tl 0 num_bc_sats 0 num_bc_tl 0 num_il_tl 0 num_op_rev_tl 0 num_rev_accts 0 num_rev_tl_bal_gt_0 0 num_sats 0 num_tl_120dpd_2m 7757 num_tl_30dpd 0 num_tl_90g_dpd_24m 0 num_tl_op_past_12m 0 pct_tl_nvr_dlq 0 percent_bc_gt_75 1511 pub_rec_bankruptcies 0 tax_liens 0 tot_hi_cred_lim 0 total_bal_ex_mort 0 total_bc_limit 0 total_il_high_credit_limit 0 revol_bal_joint 126443 sec_app_fico_range_low 126443 sec_app_fico_range_high 126443 sec_app_earliest_cr_line 126443 sec_app_inq_last_6mths 126443 sec_app_mort_acc 126443 sec_app_open_acc 126443 sec_app_revol_util 126512 sec_app_open_act_il 126443 sec_app_num_rev_accts 126443 sec_app_chargeoff_within_12_mths 126443 sec_app_collections_12_mths_ex_med 126443 sec_app_mths_since_last_major_derog 128013 hardship_flag 0 hardship_type 126877 hardship_reason 126877 hardship_status 126877 deferral_term 126877 hardship_amount 126877 hardship_start_date 126877 hardship_end_date 126877 payment_plan_start_date 126877 hardship_length 126877 hardship_dpd 126877 hardship_loan_status 126877 orig_projected_additional_accrued_interest 127919 hardship_payoff_balance_amount 126877 hardship_last_payment_amount 126877 disbursement_method 0 debt_settlement_flag 0 debt_settlement_flag_date 110952 settlement_status 110952 settlement_date 110952 settlement_amount 110952 settlement_percentage 110952 settlement_term 110952 emp_length_int 0 term_int 0 earliest_cr_line_date 0 mth_earliest_cr_line_date 0 grade:A 0 grade:B 0 grade:C 0 grade:D 0 grade:E 0 grade:F 0 grade:G 0 sub_grade:A1 0 sub_grade:A2 0 sub_grade:A3 0 sub_grade:A4 0 sub_grade:A5 0 sub_grade:B1 0 sub_grade:B2 0 sub_grade:B3 0 sub_grade:B4 0 sub_grade:B5 0 sub_grade:C1 0 sub_grade:C2 0 sub_grade:C3 0 sub_grade:C4 0 sub_grade:C5 0 sub_grade:D1 0 sub_grade:D2 0 sub_grade:D3 0 sub_grade:D4 0 sub_grade:D5 0 sub_grade:E1 0 sub_grade:E2 0 sub_grade:E3 0 sub_grade:E4 0 sub_grade:E5 0 sub_grade:F1 0 sub_grade:F2 0 sub_grade:F3 0 sub_grade:F4 0 sub_grade:F5 0 sub_grade:G1 0 sub_grade:G2 0 sub_grade:G3 0 sub_grade:G4 0 sub_grade:G5 0 home_ownership:ANY 0 home_ownership:MORTGAGE 0 home_ownership:NONE 0 home_ownership:OWN 0 home_ownership:RENT 0 verification_status:Not Verified 0 verification_status:Source Verified 0 verification_status:Verified 0 loan_status:Charged Off 0 loan_status:Current 0 loan_status:Default 0 loan_status:Fully Paid 0 loan_status:In Grace Period 0 loan_status:Late (16-30 days) 0 loan_status:Late (31-120 days) 0 purpose:car 0 purpose:credit_card 0 purpose:debt_consolidation 0 purpose:educational 0 purpose:home_improvement 0 purpose:house 0 purpose:major_purchase 0 purpose:medical 0 purpose:moving 0 purpose:other 0 purpose:renewable_energy 0 purpose:small_business 0 purpose:vacation 0 purpose:wedding 0 addr_state:AK 0 addr_state:AL 0 addr_state:AR 0 addr_state:AZ 0 addr_state:CA 0 addr_state:CO 0 addr_state:CT 0 addr_state:DC 0 addr_state:DE 0 addr_state:FL 0 addr_state:GA 0 addr_state:HI 0 addr_state:ID 0 addr_state:IL 0 addr_state:IN 0 addr_state:KS 0 addr_state:KY 0 addr_state:LA 0 addr_state:MA 0 addr_state:MD 0 addr_state:ME 0 addr_state:MI 0 addr_state:MN 0 addr_state:MO 0 addr_state:MS 0 addr_state:MT 0 addr_state:NC 0 addr_state:ND 0 addr_state:NE 0 addr_state:NH 0 addr_state:NJ 0 addr_state:NM 0 addr_state:NV 0 addr_state:NY 0 addr_state:OH 0 addr_state:OK 0 addr_state:OR 0 addr_state:PA 0 addr_state:RI 0 addr_state:SC 0 addr_state:SD 0 addr_state:TN 0 addr_state:TX 0 addr_state:UT 0 addr_state:VA 0 addr_state:VT 0 addr_state:WA 0 addr_state:WI 0 addr_state:WV 0 addr_state:WY 0 initial_list_status:f 0 initial_list_status:w 0 good_bad 0 dtype: int64
In [15]:
loan_data_defaults['mths_since_last_delinq'].fillna(0,inplace = True)
C:\Users\computer\AppData\Local\Temp/ipykernel_2444/3255895574.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
loan_data_defaults['mths_since_last_delinq'].fillna(0,inplace = True)
C:\Users\computer\AppData\Local\Temp/ipykernel_2444/3255895574.py:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
loan_data_defaults['mths_since_last_delinq'].fillna(0,inplace = True)
In [16]:
loan_data_defaults['mths_since_last_record'].fillna(0,inplace = True)
C:\Users\computer\AppData\Local\Temp/ipykernel_2444/4068106314.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
loan_data_defaults['mths_since_last_record'].fillna(0,inplace = True)
C:\Users\computer\AppData\Local\Temp/ipykernel_2444/4068106314.py:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
loan_data_defaults['mths_since_last_record'].fillna(0,inplace = True)
In [17]:
loan_data_defaults['recovery_rate'] = loan_data_defaults['recoveries'] / loan_data_defaults['funded_amnt']
C:\Users\computer\AppData\Local\Temp/ipykernel_2444/2670870593.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy loan_data_defaults['recovery_rate'] = loan_data_defaults['recoveries'] / loan_data_defaults['funded_amnt']
In [18]:
loan_data_defaults['recovery_rate'].describe()
Out[18]:
count 129187.000000 mean 0.075247 std 0.096644 min 0.000000 25% 0.000000 50% 0.062286 75% 0.106881 max 1.294209 Name: recovery_rate, dtype: float64
In [19]:
loan_data_defaults['recovery_rate'] = np.where(loan_data_defaults['recovery_rate'] > 1,1, loan_data_defaults['recovery_rate'])
loan_data_defaults['recovery_rate'] = np.where(loan_data_defaults['recovery_rate'] < 0,0, loan_data_defaults['recovery_rate'])
C:\Users\computer\AppData\Local\Temp/ipykernel_2444/3885959169.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy loan_data_defaults['recovery_rate'] = np.where(loan_data_defaults['recovery_rate'] > 1,1, loan_data_defaults['recovery_rate']) C:\Users\computer\AppData\Local\Temp/ipykernel_2444/3885959169.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy loan_data_defaults['recovery_rate'] = np.where(loan_data_defaults['recovery_rate'] < 0,0, loan_data_defaults['recovery_rate'])
In [20]:
loan_data_defaults['recovery_rate'].describe()
Out[20]:
count 129187.000000 mean 0.075229 std 0.096462 min 0.000000 25% 0.000000 50% 0.062286 75% 0.106881 max 1.000000 Name: recovery_rate, dtype: float64
In [21]:
loan_data_defaults['CCF'] = (loan_data_defaults['funded_amnt'] - loan_data_defaults['total_rec_prncp']) / loan_data_defaults['funded_amnt']
C:\Users\computer\AppData\Local\Temp/ipykernel_2444/1145321474.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy loan_data_defaults['CCF'] = (loan_data_defaults['funded_amnt'] - loan_data_defaults['total_rec_prncp']) / loan_data_defaults['funded_amnt']
In [22]:
loan_data_defaults['CCF'].describe()
Out[22]:
count 129187.000000 mean 0.706098 std 0.213790 min 0.000000 25% 0.585246 50% 0.754280 75% 0.875441 max 1.000000 Name: CCF, dtype: float64
In [23]:
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
In [24]:
plt.hist(loan_data_defaults['recovery_rate'], bins =50)
Out[24]:
(array([4.9588e+04, 5.7660e+03, 7.9840e+03, 1.2412e+04, 1.7534e+04,
9.0020e+03, 9.6800e+03, 3.8100e+03, 2.6340e+03, 2.0750e+03,
1.4880e+03, 6.9900e+02, 5.5600e+02, 5.5400e+02, 5.3500e+02,
5.7100e+02, 5.5200e+02, 5.5900e+02, 4.7200e+02, 4.8400e+02,
4.3500e+02, 3.3300e+02, 2.8200e+02, 1.8600e+02, 1.4800e+02,
1.2400e+02, 1.0500e+02, 8.9000e+01, 5.2000e+01, 5.1000e+01,
5.2000e+01, 5.0000e+01, 3.3000e+01, 2.1000e+01, 2.0000e+01,
1.3000e+01, 1.7000e+01, 1.7000e+01, 1.7000e+01, 2.1000e+01,
1.3000e+01, 1.4000e+01, 1.8000e+01, 1.4000e+01, 1.2000e+01,
7.0000e+00, 1.0000e+01, 1.5000e+01, 1.3000e+01, 5.0000e+01]),
array([0. , 0.02, 0.04, 0.06, 0.08, 0.1 , 0.12, 0.14, 0.16, 0.18, 0.2 ,
0.22, 0.24, 0.26, 0.28, 0.3 , 0.32, 0.34, 0.36, 0.38, 0.4 , 0.42,
0.44, 0.46, 0.48, 0.5 , 0.52, 0.54, 0.56, 0.58, 0.6 , 0.62, 0.64,
0.66, 0.68, 0.7 , 0.72, 0.74, 0.76, 0.78, 0.8 , 0.82, 0.84, 0.86,
0.88, 0.9 , 0.92, 0.94, 0.96, 0.98, 1. ]),
<BarContainer object of 50 artists>)
In [25]:
plt.hist(loan_data_defaults['CCF'], bins =100)
Out[25]:
(array([ 52., 26., 33., 274., 34., 40., 379., 67., 50.,
354., 191., 63., 275., 314., 111., 296., 382., 182.,
311., 386., 272., 327., 472., 335., 411., 546., 362.,
445., 519., 389., 481., 600., 480., 577., 643., 551.,
579., 716., 654., 671., 716., 712., 758., 829., 786.,
865., 824., 917., 910., 957., 967., 1062., 1088., 1031.,
1234., 1205., 1304., 1536., 1310., 1499., 1601., 1569., 1647.,
1721., 1832., 1938., 1768., 2024., 2090., 2071., 2164., 2295.,
2161., 2302., 2192., 2291., 2311., 2293., 2562., 2402., 2590.,
2630., 2549., 3133., 2800., 3116., 3077., 2879., 3393., 2578.,
3397., 2954., 2704., 3352., 2065., 3371., 1805., 2585., 1068.,
1547.]),
array([0. , 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ,
0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 , 0.21,
0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 , 0.31, 0.32,
0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 , 0.41, 0.42, 0.43,
0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 , 0.51, 0.52, 0.53, 0.54,
0.55, 0.56, 0.57, 0.58, 0.59, 0.6 , 0.61, 0.62, 0.63, 0.64, 0.65,
0.66, 0.67, 0.68, 0.69, 0.7 , 0.71, 0.72, 0.73, 0.74, 0.75, 0.76,
0.77, 0.78, 0.79, 0.8 , 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87,
0.88, 0.89, 0.9 , 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98,
0.99, 1. ]),
<BarContainer object of 100 artists>)
In [26]:
loan_data_defaults['recovery_rate_0_1'] = np.where(loan_data_defaults['recovery_rate'] ==0, 0 , 1 )
C:\Users\computer\AppData\Local\Temp/ipykernel_2444/948371573.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy loan_data_defaults['recovery_rate_0_1'] = np.where(loan_data_defaults['recovery_rate'] ==0, 0 , 1 )
In [27]:
loan_data_defaults['recovery_rate_0_1']
Out[27]:
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1 1048457 1 1048458 0 1048481 1 1048493 1 1048500 1 1048508 1 1048519 1 1048535 0 1048544 1 1048545 1 1048548 0 1048561 1 1048570 1 Name: recovery_rate_0_1, dtype: int32
In [28]:
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
# Assuming loan_data_defaults is already defined and loaded with your data
# loan_data_defaults = pd.read_csv('path_to_your_data.csv') # Load your data here
# Split the data into training and testing sets
lgd_inputs_stage_1_train, lgd_inputs_stage_1_test, lgd_target_stage_1_train, lgd_target_stage_1_test = train_test_split(
loan_data_defaults.drop([ 'good_bad','recovery_rate', 'recovery_rate_0_1', 'CCF'], axis=1),
loan_data_defaults['recovery_rate_0_1'],
test_size=0.2,
random_state=42
)
# Print shapes to verify
print("Training features shape:", lgd_inputs_stage_1_train.shape)
print("Training target shape:", lgd_target_stage_1_train.shape)
print("Testing features shape:", lgd_inputs_stage_1_test.shape)
print("Testing target shape:", lgd_target_stage_1_test.shape)
Training features shape: (103349, 278) Training target shape: (103349,) Testing features shape: (25838, 278) Testing target shape: (25838,)
In [29]:
features_all= ['grade:A',
'grade:B',
'grade:C',
'grade:D',
'grade:E',
'grade:F',
'grade:G',
'home_ownership:MORTGAGE',
'home_ownership:NONE',
'home_ownership:OWN',
'home_ownership:RENT',
'verification_status:Not Verified',
'verification_status:Source Verified',
'verification_status:Verified',
'purpose:car',
'purpose:credit_card',
'purpose:debt_consolidation',
'purpose:educational',
'purpose:home_improvement',
'purpose:house',
'purpose:moving',
'purpose:other',
'purpose:renewable_energy',
'purpose:small_business',
'purpose:vacation',
'purpose:wedding',
'initial_list_status:f',
'initial_list_status:w',
'term_int',
'emp_length_int',
'funded_amnt',
'int_rate',
'installment',
'annual_inc',
'dti',
'delinq_2yrs',
'inq_last_6mths',
'mths_since_last_delinq',
'mths_since_last_record',
'open_acc',
'pub_rec',
'total_acc',
'acc_now_delinq',
'total_rev_hi_lim']
In [30]:
features_reference_cat = ['grade:G',
'home_ownership:RENT',
'verification_status:Verified',
'purpose:credit_card',
'initial_list_status:f'
]
In [31]:
lgd_inputs_stage_1_train = lgd_inputs_stage_1_train[features_all]
In [32]:
lgd_inputs_stage_1_train = lgd_inputs_stage_1_train.drop(features_reference_cat, axis =1)
In [33]:
from sklearn.impute import SimpleImputer
from sklearn import linear_model
import numpy as np
import scipy.stats as stat
import pandas as pd
class LogisticRegression_with_p_values:
def __init__(self, *args, **kwargs):
self.model = linear_model.LogisticRegression(*args, **kwargs)
def fit(self, X, y):
# Handle missing values by imputing with the mean
imputer = SimpleImputer(strategy='mean') # You can also use 'median' or 'most_frequent'
X = imputer.fit_transform(X)
# Fit the logistic regression model
self.model.fit(X, y)
# Calculate p-values
denom = (2.0 * (1.0 + np.cosh(self.model.decision_function(X))))
denom = np.tile(denom, (X.shape[1], 1)).T
F_ij = np.dot((X / denom).T, X)
F_ij += np.eye(F_ij.shape[0]) * 1e-4 # Add small regularization term
Cramer_Rao = np.linalg.inv(F_ij)
sigma_estimates = np.sqrt(np.diagonal(Cramer_Rao))
z_scores = self.model.coef_[0] / sigma_estimates
p_values = [stat.norm.sf(abs(x)) * 2 for x in z_scores]
# Store results
self.coef_ = self.model.coef_
self.intercept_ = self.model.intercept_
self.p_values = p_values
# Example usage
# Assuming you already have lgd_inputs_stage_1_train and lgd_target_stage_1_train data
# Initialize and fit the custom logistic regression model
reg_lgd_st_1 = LogisticRegression_with_p_values()
reg_lgd_st_1.fit(lgd_inputs_stage_1_train, lgd_target_stage_1_train)
# Create a summary table with feature names, coefficients, and p-values
feature_name = lgd_inputs_stage_1_train.columns.values
summary_table = pd.DataFrame(columns=['feature_name'], data=feature_name)
summary_table['Coefficients'] = reg_lgd_st_1.coef_.T # Use .T for transpose
# Format p-values to full precision
summary_table['p_values'] = ["{:.10f}".format(p) for p in reg_lgd_st_1.p_values]
# Add intercept
summary_table.index = summary_table.index + 1
summary_table.loc[0] = ['Intercept', reg_lgd_st_1.intercept_[0], None]
summary_table = summary_table.sort_index()
# Print the summary table
print(summary_table)
feature_name Coefficients p_values 0 Intercept 1.703803e-04 None 1 grade:A -1.756219e-04 0.9969517490 2 grade:B -2.188782e-05 0.9995242723 3 grade:C 2.305734e-04 0.9947367228 4 grade:D 2.741044e-05 0.9994098911 5 grade:E 6.442150e-05 0.9987547538 6 grade:F 3.896695e-05 0.9993921315 7 home_ownership:MORTGAGE 6.708141e-05 0.9963804843 8 home_ownership:NONE 0.000000e+00 1.0000000000 9 home_ownership:OWN 7.285305e-05 0.9972941853 10 verification_status:Not Verified -1.745278e-04 0.9925307520 11 verification_status:Source Verified 1.685601e-04 0.9910974691 12 purpose:car -7.724271e-06 0.9999195897 13 purpose:debt_consolidation 1.708695e-04 0.9915990049 14 purpose:educational 0.000000e+00 1.0000000000 15 purpose:home_improvement -5.058007e-06 0.9998685933 16 purpose:house 6.769935e-07 0.9999940417 17 purpose:moving 1.166809e-05 0.9998724233 18 purpose:other 1.034133e-05 0.9997309619 19 purpose:renewable_energy -5.738923e-08 0.9999997972 20 purpose:small_business 1.090852e-06 0.9999851507 21 purpose:vacation 2.784547e-06 0.9999728016 22 purpose:wedding 1.201000e-07 0.9999999532 23 initial_list_status:w 2.611613e-04 0.9855964393 24 term_int 5.442574e-03 0.0000797329 25 emp_length_int 2.306293e-03 0.1758984537 26 funded_amnt -3.400098e-05 0.0000000000 27 int_rate 2.705732e-03 0.3815281569 28 installment 1.273023e-03 0.0000000000 29 annual_inc 2.387504e-06 0.0000000000 30 dti 1.584949e-03 0.0187015013 31 delinq_2yrs 4.841318e-04 0.9451779814 32 inq_last_6mths 3.680460e-04 0.9587551009 33 mths_since_last_delinq 5.727927e-04 0.0470948232 34 mths_since_last_record -1.653521e-03 0.0000000014 35 open_acc 1.380151e-03 0.4134774424 36 pub_rec 1.613422e-04 0.9891732454 37 total_acc 4.795450e-03 0.0000000021 38 acc_now_delinq 1.546255e-05 0.9998357040 39 total_rev_hi_lim -3.340974e-06 0.0000000000
C:\Users\computer\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
In [34]:
lgd_inputs_stage_1_test = lgd_inputs_stage_1_test[features_all]
In [35]:
lgd_inputs_stage_1_test = lgd_inputs_stage_1_test.drop(features_reference_cat, axis =1)
In [36]:
lgd_inputs_stage_1_test_clean = lgd_inputs_stage_1_test.dropna()
# Predict with the cleaned test data
y_hat_test_lgd_stage_1 = reg_lgd_st_1.model.predict(lgd_inputs_stage_1_test_clean)
C:\Users\computer\anaconda3\lib\site-packages\sklearn\base.py:486: UserWarning: X has feature names, but LogisticRegression was fitted without feature names warnings.warn(
In [37]:
y_hat_test_lgd_stage_1
Out[37]:
array([1, 1, 1, ..., 1, 1, 1])
In [38]:
y_hat_test_proba_lgd_stage_1 = reg_lgd_st_1.model.predict_proba(lgd_inputs_stage_1_test_clean)
C:\Users\computer\anaconda3\lib\site-packages\sklearn\base.py:486: UserWarning: X has feature names, but LogisticRegression was fitted without feature names warnings.warn(
In [39]:
y_hat_test_proba_lgd_stage_1
Out[39]:
array([[0.36178492, 0.63821508],
[0.34224258, 0.65775742],
[0.39611566, 0.60388434],
...,
[0.32806725, 0.67193275],
[0.36589369, 0.63410631],
[0.3404167 , 0.6595833 ]])
In [40]:
y_hat_test_proba_lgd_stage_1 = y_hat_test_proba_lgd_stage_1[:][:,1]
y_hat_test_proba_lgd_stage_1
Out[40]:
array([0.63821508, 0.65775742, 0.60388434, ..., 0.67193275, 0.63410631,
0.6595833 ])
In [41]:
lgd_stage_2_data = loan_data_defaults[loan_data_defaults['recovery_rate_0_1']==1]
In [42]:
from sklearn.model_selection import train_test_split
lgd_inputs_stage_2_train, lgd_inputs_stage_2_test, lgd_target_stage_2_train, lgd_target_stage_2_test = train_test_split(
lgd_stage_2_data.drop(['good_bad', 'recovery_rate', 'recovery_rate_0_1', 'CCF'], axis=1),
lgd_stage_2_data['recovery_rate'],
test_size=0.2,
random_state=42
)
In [43]:
from sklearn import linear_model
from sklearn.metrics import mean_squared_error,r2_score
In [44]:
import scipy.stats as stat
class LinearRegression(linear_model.LinearRegression):
def __init__(self, fit_intercept=True, normalize=False, copy_X=True,
n_jobs=1):
self.fit_intercept = fit_intercept
self.normalize = normalize
self.copy_X = copy_X
self.n_jobs = n_jobs
def fit(self, X, y, n_jobs=1):
self = super(LinearRegression, self).fit(X, y, n_jobs)
sse = np.sum((self.predict(X) - y) ** 2, axis=0) / float(X.shape[0] - X.shape[1])
se = np.array([np.sqrt(np.diagonal(sse * np.linalg.inv(np.dot(X.T, X))))])
self.t = self.coef_ / se
self.p = np.squeeze(2 * (1 - stat.t.cdf(np.abs(self.t), y.shape[0] - X.shape[1])))
return self
In [45]:
lgd_inputs_stage_2_train = lgd_inputs_stage_2_train[features_all]
In [46]:
lgd_inputs_stage_2_train = lgd_inputs_stage_2_train.drop(features_reference_cat, axis =1)
In [47]:
# Ensure you're using the sklearn LinearRegression class
from sklearn.linear_model import LinearRegression
# Combine inputs and target into one DataFrame to drop rows with NaN in both simultaneously
data_combined = pd.concat([lgd_inputs_stage_2_train, lgd_target_stage_2_train], axis=1)
# Drop rows with NaN values from the combined data
data_combined_cleaned = data_combined.dropna()
# Split the data back into inputs and target
lgd_inputs_stage_2_train_cleaned = data_combined_cleaned.drop(columns=lgd_target_stage_2_train.name)
lgd_target_stage_2_train_cleaned = data_combined_cleaned[lgd_target_stage_2_train.name]
# Now fit the model with the cleaned data
reg_lgd_st_2 = LinearRegression()
reg_lgd_st_2.fit(lgd_inputs_stage_2_train_cleaned, lgd_target_stage_2_train_cleaned)
# You can now use the model to make predictions if you have a test set
# predictions = reg_lgd_st_2.predict(lgd_inputs_stage_2_test_cleaned)
Out[47]:
LinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LinearRegression()
In [48]:
feature_name = lgd_inputs_stage_2_train.columns.values
In [49]:
import statsmodels.api as sm
import pandas as pd
import numpy as np
# Add constant to inputs for intercept term (statsmodels needs this explicitly)
X = sm.add_constant(lgd_inputs_stage_2_train_cleaned)
y = lgd_target_stage_2_train_cleaned
# Fit the model using statsmodels
model = sm.OLS(y, X)
results = model.fit()
# Get coefficients and p-values
coefficients = results.params
p_values = results.pvalues
# Create a summary table
summary_table = pd.DataFrame({
'Feature name': X.columns,
'Coefficients': coefficients,
'p_values': p_values.round(3)
})
# Remove the 'const' row from the table if it exists
summary_table = summary_table[summary_table['Feature name'] != 'const']
# Add intercept at the top of the table
intercept_row = pd.DataFrame({'Feature name': ['Intercept'], 'Coefficients': [coefficients[0]], 'p_values': [p_values[0]]})
summary_table = pd.concat([intercept_row, summary_table]).reset_index(drop=True)
# Display the table
print(summary_table)
Feature name Coefficients p_values 0 Intercept 3.167942e-02 0.000186 1 grade:A 3.665550e-02 0.000000 2 grade:B 2.172757e-02 0.000000 3 grade:C 1.600715e-02 0.001000 4 grade:D 1.283965e-02 0.002000 5 grade:E 8.856359e-03 0.013000 6 grade:F 5.454970e-03 0.097000 7 home_ownership:MORTGAGE 3.692868e-03 0.000000 8 home_ownership:NONE -1.079955e-13 0.716000 9 home_ownership:OWN 2.952427e-03 0.018000 10 verification_status:Not Verified 6.503685e-04 0.555000 11 verification_status:Source Verified -2.022783e-03 0.020000 12 purpose:car 2.184470e-03 0.637000 13 purpose:debt_consolidation 3.416965e-05 0.971000 14 purpose:educational 1.075933e-13 0.716000 15 purpose:home_improvement 8.215795e-04 0.646000 16 purpose:house 1.161879e-03 0.819000 17 purpose:moving 3.949910e-05 0.992000 18 purpose:other 1.432287e-03 0.424000 19 purpose:renewable_energy -1.259890e-02 0.330000 20 purpose:small_business -1.976226e-03 0.554000 21 purpose:vacation 1.279063e-02 0.006000 22 purpose:wedding 3.567170e-02 0.714000 23 initial_list_status:w -7.295619e-03 0.000000 24 term_int 7.299396e-04 0.000000 25 emp_length_int 2.218726e-04 0.026000 26 funded_amnt -5.003832e-07 0.102000 27 int_rate 2.372935e-03 0.000000 28 installment -2.224022e-06 0.816000 29 annual_inc 1.562286e-08 0.002000 30 dti 2.988877e-04 0.000000 31 delinq_2yrs -1.049724e-03 0.007000 32 inq_last_6mths -4.432327e-04 0.279000 33 mths_since_last_delinq -4.261937e-05 0.011000 34 mths_since_last_record -6.825894e-06 0.693000 35 open_acc -8.520137e-04 0.000000 36 pub_rec -2.733927e-03 0.000000 37 total_acc 5.058336e-04 0.000000 38 acc_now_delinq -2.468156e-03 0.537000 39 total_rev_hi_lim 1.371529e-07 0.000000
C:\Users\computer\AppData\Local\Temp/ipykernel_2444/711133824.py:28: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
intercept_row = pd.DataFrame({'Feature name': ['Intercept'], 'Coefficients': [coefficients[0]], 'p_values': [p_values[0]]})
In [50]:
lgd_inputs_stage_2_test = lgd_inputs_stage_2_test[features_all]
In [51]:
lgd_inputs_stage_2_test = lgd_inputs_stage_2_test.drop(features_reference_cat, axis = 1)
In [52]:
lgd_inputs_stage_2_test.columns.values
Out[52]:
array(['grade:A', 'grade:B', 'grade:C', 'grade:D', 'grade:E', 'grade:F',
'home_ownership:MORTGAGE', 'home_ownership:NONE',
'home_ownership:OWN', 'verification_status:Not Verified',
'verification_status:Source Verified', 'purpose:car',
'purpose:debt_consolidation', 'purpose:educational',
'purpose:home_improvement', 'purpose:house', 'purpose:moving',
'purpose:other', 'purpose:renewable_energy',
'purpose:small_business', 'purpose:vacation', 'purpose:wedding',
'initial_list_status:w', 'term_int', 'emp_length_int',
'funded_amnt', 'int_rate', 'installment', 'annual_inc', 'dti',
'delinq_2yrs', 'inq_last_6mths', 'mths_since_last_delinq',
'mths_since_last_record', 'open_acc', 'pub_rec', 'total_acc',
'acc_now_delinq', 'total_rev_hi_lim'], dtype=object)
In [53]:
# Drop rows with NaN values in the test data
lgd_inputs_stage_2_test_cleaned = lgd_inputs_stage_2_test.dropna()
# Now make predictions
y_hat_test_lgd_stage_2 = reg_lgd_st_2.predict(lgd_inputs_stage_2_test_cleaned)
In [54]:
lgd_target_stage_2_test_temp = lgd_target_stage_2_test
In [55]:
lgd_target_stage_2_test_temp = lgd_target_stage_2_test_temp.reset_index(drop = True)
In [56]:
pd.concat([lgd_target_stage_2_test_temp, pd.DataFrame(y_hat_test_lgd_stage_2)], axis = 1).corr()
Out[56]:
| recovery_rate | 0 | |
|---|---|---|
| recovery_rate | 1.000000 | 0.023794 |
| 0 | 0.023794 | 1.000000 |
In [88]:
import seaborn as sns
import matplotlib.pyplot as plt
# Compute the residuals
residuals = lgd_target_stage_2_test_cleaned - y_hat_test_lgd_stage_2
# Plot the distribution of residuals using histplot (replaces distplot)
sns.histplot(residuals, kde=True)
plt.xlabel('Residuals')
plt.title('Distribution of Residuals')
plt.show()
--------------------------------------------------------------------------- OptionError Traceback (most recent call last) ~\AppData\Local\Temp/ipykernel_2444/3193588322.py in <module> 6 7 # Plot the distribution of residuals using histplot (replaces distplot) ----> 8 sns.histplot(residuals, kde=True) 9 plt.xlabel('Residuals') 10 plt.title('Distribution of Residuals') ~\anaconda3\lib\site-packages\seaborn\distributions.py in histplot(data, x, y, hue, weights, stat, bins, binwidth, binrange, discrete, cumulative, common_bins, common_norm, multiple, element, fill, shrink, kde, kde_kws, line_kws, thresh, pthresh, pmax, cbar, cbar_ax, cbar_kws, palette, hue_order, hue_norm, color, log_scale, legend, ax, **kwargs) 1460 if p.univariate: 1461 -> 1462 p.plot_univariate_histogram( 1463 multiple=multiple, 1464 element=element, ~\anaconda3\lib\site-packages\seaborn\distributions.py in plot_univariate_histogram(self, multiple, element, fill, common_norm, common_bins, shrink, kde, kde_kws, color, legend, line_kws, estimate_kws, **plot_kws) 416 kde_kws["cumulative"] = estimate_kws["cumulative"] 417 log_scale = self._log_scaled(self.data_variable) --> 418 densities = self._compute_univariate_density( 419 self.data_variable, 420 common_norm, ~\anaconda3\lib\site-packages\seaborn\distributions.py in _compute_univariate_density(self, data_variable, common_norm, common_grid, estimate_kws, log_scale, warn_singular) 301 densities = {} 302 --> 303 for sub_vars, sub_data in self.iter_data("hue", from_comp_data=True): 304 305 # Extract the data points from this sub set and remove nulls ~\anaconda3\lib\site-packages\seaborn\_core.py in iter_data(self, grouping_vars, reverse, from_comp_data) 981 982 if from_comp_data: --> 983 data = self.comp_data 984 else: 985 data = self.plot_data ~\anaconda3\lib\site-packages\seaborn\_core.py in comp_data(self) 1052 # Use the converter assigned to the axis to get a float representation 1053 # of the data, passing np.nan or pd.NA through (pd.NA becomes np.nan) -> 1054 with pd.option_context('mode.use_inf_as_null', True): 1055 orig = self.plot_data[var].dropna() 1056 comp_col = pd.Series(index=orig.index, dtype=float, name=var) ~\anaconda3\lib\site-packages\pandas\_config\config.py in __enter__(self) 478 if not isinstance(cursor, dict): 479 raise OptionError(msg.format(option=".".join(path[:-1]))) --> 480 481 cursor[path[-1]] = defval # initialize 482 ~\anaconda3\lib\site-packages\pandas\_config\config.py in <listcomp>(.0) 478 if not isinstance(cursor, dict): 479 raise OptionError(msg.format(option=".".join(path[:-1]))) --> 480 481 cursor[path[-1]] = defval # initialize 482 ~\anaconda3\lib\site-packages\pandas\_config\config.py in _get_option(pat, silent) 144 def _describe_option(pat: str = "", _print_desc: bool = True): 145 --> 146 keys = _select_options(pat) 147 if len(keys) == 0: 148 raise OptionError("No such keys(s)") ~\anaconda3\lib\site-packages\pandas\_config\config.py in _get_single_key(pat, silent) 130 o.validator(v) 131 --> 132 # walk the nested dict 133 root, k = _get_root(key) 134 root[k] = v OptionError: "No such keys(s): 'mode.use_inf_as_null'"
In [81]:
# Drop NaN values in both the features and the target data
data_combined_test = pd.concat([lgd_inputs_stage_2_test, lgd_target_stage_2_test], axis=1)
data_combined_test_cleaned = data_combined_test.dropna()
# Split the cleaned data back into features and target
lgd_inputs_stage_2_test_cleaned = data_combined_test_cleaned.drop(columns=lgd_target_stage_2_test.name)
lgd_target_stage_2_test_cleaned = data_combined_test_cleaned[lgd_target_stage_2_test.name]
# Now make predictions using the cleaned data
y_hat_test_lgd_stage_2 = reg_lgd_st_2.predict(lgd_inputs_stage_2_test_cleaned)
# Plot the distribution of residuals (errors)
sns.distplot(lgd_target_stage_2_test_cleaned - y_hat_test_lgd_stage_2)
C:\Users\computer\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
--------------------------------------------------------------------------- OptionError Traceback (most recent call last) ~\AppData\Local\Temp/ipykernel_2444/1738210295.py in <module> 11 12 # Plot the distribution of residuals (errors) ---> 13 sns.distplot(lgd_target_stage_2_test_cleaned - y_hat_test_lgd_stage_2) 14 15 ~\anaconda3\lib\site-packages\seaborn\distributions.py in distplot(a, bins, hist, kde, rug, fit, hist_kws, kde_kws, rug_kws, fit_kws, color, vertical, norm_hist, axlabel, label, ax, x) 2685 if kde: 2686 kde_color = kde_kws.pop("color", color) -> 2687 kdeplot(a, vertical=vertical, ax=ax, color=kde_color, **kde_kws) 2688 if kde_color != color: 2689 kde_kws["color"] = kde_color ~\anaconda3\lib\site-packages\seaborn\_decorators.py in inner_f(*args, **kwargs) 44 ) 45 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)}) ---> 46 return f(**kwargs) 47 return inner_f 48 ~\anaconda3\lib\site-packages\seaborn\distributions.py in kdeplot(x, y, shade, vertical, kernel, bw, gridsize, cut, clip, legend, cumulative, shade_lowest, cbar, cbar_ax, cbar_kws, ax, weights, hue, palette, hue_order, hue_norm, multiple, common_norm, common_grid, levels, thresh, bw_method, bw_adjust, log_scale, color, fill, data, data2, warn_singular, **kwargs) 1768 plot_kws["color"] = color 1769 -> 1770 p.plot_univariate_density( 1771 multiple=multiple, 1772 common_norm=common_norm, ~\anaconda3\lib\site-packages\seaborn\distributions.py in plot_univariate_density(self, multiple, common_norm, common_grid, warn_singular, fill, legend, estimate_kws, **plot_kws) 926 927 # Do the computation --> 928 densities = self._compute_univariate_density( 929 self.data_variable, 930 common_norm, ~\anaconda3\lib\site-packages\seaborn\distributions.py in _compute_univariate_density(self, data_variable, common_norm, common_grid, estimate_kws, log_scale, warn_singular) 301 densities = {} 302 --> 303 for sub_vars, sub_data in self.iter_data("hue", from_comp_data=True): 304 305 # Extract the data points from this sub set and remove nulls ~\anaconda3\lib\site-packages\seaborn\_core.py in iter_data(self, grouping_vars, reverse, from_comp_data) 981 982 if from_comp_data: --> 983 data = self.comp_data 984 else: 985 data = self.plot_data ~\anaconda3\lib\site-packages\seaborn\_core.py in comp_data(self) 1052 # Use the converter assigned to the axis to get a float representation 1053 # of the data, passing np.nan or pd.NA through (pd.NA becomes np.nan) -> 1054 with pd.option_context('mode.use_inf_as_null', True): 1055 orig = self.plot_data[var].dropna() 1056 comp_col = pd.Series(index=orig.index, dtype=float, name=var) ~\anaconda3\lib\site-packages\pandas\_config\config.py in __enter__(self) 478 479 def __enter__(self) -> None: --> 480 self.undo = [(pat, _get_option(pat)) for pat, val in self.ops] 481 482 for pat, val in self.ops: ~\anaconda3\lib\site-packages\pandas\_config\config.py in <listcomp>(.0) 478 479 def __enter__(self) -> None: --> 480 self.undo = [(pat, _get_option(pat)) for pat, val in self.ops] 481 482 for pat, val in self.ops: ~\anaconda3\lib\site-packages\pandas\_config\config.py in _get_option(pat, silent) 144 145 def _get_option(pat: str, silent: bool = False) -> Any: --> 146 key = _get_single_key(pat, silent) 147 148 # walk the nested dict ~\anaconda3\lib\site-packages\pandas\_config\config.py in _get_single_key(pat, silent) 130 if not silent: 131 _warn_if_deprecated(pat) --> 132 raise OptionError(f"No such keys(s): {repr(pat)}") 133 if len(keys) > 1: 134 raise OptionError("Pattern matched multiple keys") OptionError: "No such keys(s): 'mode.use_inf_as_null'"
In [59]:
# Drop NaN values from the features in the test data
lgd_inputs_stage_1_test_cleaned = lgd_inputs_stage_1_test.dropna()
# Now make predictions
y_hat_test_lgd_stage_2_all = reg_lgd_st_2.predict(lgd_inputs_stage_1_test_cleaned)
In [89]:
y_hat_test_lgd_stage_2_all
Out[89]:
array([0.11786791, 0.14377634, 0.11262064, ..., 0.09261269, 0.13816447,
0.12864535])
In [90]:
y_hat_test_lgd = y_hat_test_lgd_stage_1 * y_hat_test_lgd_stage_2_all
In [91]:
pd.DataFrame(y_hat_test_lgd).describe()
Out[91]:
| 0 | |
|---|---|
| count | 25828.000000 |
| mean | 0.117032 |
| std | 0.014581 |
| min | 0.000000 |
| 25% | 0.106772 |
| 50% | 0.116437 |
| 75% | 0.126764 |
| max | 0.231911 |
In [92]:
from sklearn.model_selection import train_test_split
ead_inputs_train, ead_inputs_test, ead_target_train, ead_target_test = train_test_split(
loan_data_defaults.drop(['good_bad', 'recovery_rate', 'recovery_rate_0_1', 'CCF'], axis=1),
loan_data_defaults['CCF'],
test_size=0.2,
random_state=42
)
In [93]:
ead_inputs_train.columns.values
Out[93]:
array(['id', 'member_id', 'loan_amnt', 'funded_amnt', 'funded_amnt_inv',
'term', 'int_rate', 'installment', 'grade', 'sub_grade',
'emp_title', 'emp_length', 'home_ownership', 'annual_inc',
'verification_status', 'issue_d', 'loan_status', 'pymnt_plan',
'url', 'desc', 'purpose', 'title', 'zip_code', 'addr_state', 'dti',
'delinq_2yrs', 'earliest_cr_line', 'fico_range_low',
'fico_range_high', 'inq_last_6mths', 'mths_since_last_delinq',
'mths_since_last_record', 'open_acc', 'pub_rec', 'revol_bal',
'revol_util', 'total_acc', 'initial_list_status', 'out_prncp',
'out_prncp_inv', 'total_pymnt', 'total_pymnt_inv',
'total_rec_prncp', 'total_rec_int', 'total_rec_late_fee',
'recoveries', 'collection_recovery_fee', 'last_pymnt_d',
'last_pymnt_amnt', 'next_pymnt_d', 'last_credit_pull_d',
'last_fico_range_high', 'last_fico_range_low',
'collections_12_mths_ex_med', 'mths_since_last_major_derog',
'policy_code', 'application_type', 'annual_inc_joint', 'dti_joint',
'verification_status_joint', 'acc_now_delinq', 'tot_coll_amt',
'tot_cur_bal', 'open_acc_6m', 'open_act_il', 'open_il_12m',
'open_il_24m', 'mths_since_rcnt_il', 'total_bal_il', 'il_util',
'open_rv_12m', 'open_rv_24m', 'max_bal_bc', 'all_util',
'total_rev_hi_lim', 'inq_fi', 'total_cu_tl', 'inq_last_12m',
'acc_open_past_24mths', 'avg_cur_bal', 'bc_open_to_buy', 'bc_util',
'chargeoff_within_12_mths', 'delinq_amnt', 'mo_sin_old_il_acct',
'mo_sin_old_rev_tl_op', 'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl',
'mort_acc', 'mths_since_recent_bc', 'mths_since_recent_bc_dlq',
'mths_since_recent_inq', 'mths_since_recent_revol_delinq',
'num_accts_ever_120_pd', 'num_actv_bc_tl', 'num_actv_rev_tl',
'num_bc_sats', 'num_bc_tl', 'num_il_tl', 'num_op_rev_tl',
'num_rev_accts', 'num_rev_tl_bal_gt_0', 'num_sats',
'num_tl_120dpd_2m', 'num_tl_30dpd', 'num_tl_90g_dpd_24m',
'num_tl_op_past_12m', 'pct_tl_nvr_dlq', 'percent_bc_gt_75',
'pub_rec_bankruptcies', 'tax_liens', 'tot_hi_cred_lim',
'total_bal_ex_mort', 'total_bc_limit',
'total_il_high_credit_limit', 'revol_bal_joint',
'sec_app_fico_range_low', 'sec_app_fico_range_high',
'sec_app_earliest_cr_line', 'sec_app_inq_last_6mths',
'sec_app_mort_acc', 'sec_app_open_acc', 'sec_app_revol_util',
'sec_app_open_act_il', 'sec_app_num_rev_accts',
'sec_app_chargeoff_within_12_mths',
'sec_app_collections_12_mths_ex_med',
'sec_app_mths_since_last_major_derog', 'hardship_flag',
'hardship_type', 'hardship_reason', 'hardship_status',
'deferral_term', 'hardship_amount', 'hardship_start_date',
'hardship_end_date', 'payment_plan_start_date', 'hardship_length',
'hardship_dpd', 'hardship_loan_status',
'orig_projected_additional_accrued_interest',
'hardship_payoff_balance_amount', 'hardship_last_payment_amount',
'disbursement_method', 'debt_settlement_flag',
'debt_settlement_flag_date', 'settlement_status',
'settlement_date', 'settlement_amount', 'settlement_percentage',
'settlement_term', 'emp_length_int', 'term_int',
'earliest_cr_line_date', 'mth_earliest_cr_line_date', 'grade:A',
'grade:B', 'grade:C', 'grade:D', 'grade:E', 'grade:F', 'grade:G',
'sub_grade:A1', 'sub_grade:A2', 'sub_grade:A3', 'sub_grade:A4',
'sub_grade:A5', 'sub_grade:B1', 'sub_grade:B2', 'sub_grade:B3',
'sub_grade:B4', 'sub_grade:B5', 'sub_grade:C1', 'sub_grade:C2',
'sub_grade:C3', 'sub_grade:C4', 'sub_grade:C5', 'sub_grade:D1',
'sub_grade:D2', 'sub_grade:D3', 'sub_grade:D4', 'sub_grade:D5',
'sub_grade:E1', 'sub_grade:E2', 'sub_grade:E3', 'sub_grade:E4',
'sub_grade:E5', 'sub_grade:F1', 'sub_grade:F2', 'sub_grade:F3',
'sub_grade:F4', 'sub_grade:F5', 'sub_grade:G1', 'sub_grade:G2',
'sub_grade:G3', 'sub_grade:G4', 'sub_grade:G5',
'home_ownership:ANY', 'home_ownership:MORTGAGE',
'home_ownership:NONE', 'home_ownership:OWN', 'home_ownership:RENT',
'verification_status:Not Verified',
'verification_status:Source Verified',
'verification_status:Verified', 'loan_status:Charged Off',
'loan_status:Current', 'loan_status:Default',
'loan_status:Fully Paid', 'loan_status:In Grace Period',
'loan_status:Late (16-30 days)', 'loan_status:Late (31-120 days)',
'purpose:car', 'purpose:credit_card', 'purpose:debt_consolidation',
'purpose:educational', 'purpose:home_improvement', 'purpose:house',
'purpose:major_purchase', 'purpose:medical', 'purpose:moving',
'purpose:other', 'purpose:renewable_energy',
'purpose:small_business', 'purpose:vacation', 'purpose:wedding',
'addr_state:AK', 'addr_state:AL', 'addr_state:AR', 'addr_state:AZ',
'addr_state:CA', 'addr_state:CO', 'addr_state:CT', 'addr_state:DC',
'addr_state:DE', 'addr_state:FL', 'addr_state:GA', 'addr_state:HI',
'addr_state:ID', 'addr_state:IL', 'addr_state:IN', 'addr_state:KS',
'addr_state:KY', 'addr_state:LA', 'addr_state:MA', 'addr_state:MD',
'addr_state:ME', 'addr_state:MI', 'addr_state:MN', 'addr_state:MO',
'addr_state:MS', 'addr_state:MT', 'addr_state:NC', 'addr_state:ND',
'addr_state:NE', 'addr_state:NH', 'addr_state:NJ', 'addr_state:NM',
'addr_state:NV', 'addr_state:NY', 'addr_state:OH', 'addr_state:OK',
'addr_state:OR', 'addr_state:PA', 'addr_state:RI', 'addr_state:SC',
'addr_state:SD', 'addr_state:TN', 'addr_state:TX', 'addr_state:UT',
'addr_state:VA', 'addr_state:VT', 'addr_state:WA', 'addr_state:WI',
'addr_state:WV', 'addr_state:WY', 'initial_list_status:f',
'initial_list_status:w'], dtype=object)
In [94]:
ead_inputs_train = ead_inputs_train[features_all]
In [95]:
ead_inputs_train = ead_inputs_train.drop(features_reference_cat, axis = 1)
In [96]:
feature_name = ead_inputs_train.columns.values
In [97]:
import pandas as pd
import numpy as np
import statsmodels.api as sm
from sklearn.linear_model import LinearRegression
# Assuming ead_inputs_train and ead_target_train are your datasets
# Drop rows with NaN values
ead_inputs_train_clean = ead_inputs_train.dropna()
# Make sure to also drop corresponding rows in the target dataset
ead_target_train_clean = ead_target_train.loc[ead_inputs_train_clean.index]
# Initialize and fit the LinearRegression model from scikit-learn for comparison
reg_ead = LinearRegression()
reg_ead.fit(ead_inputs_train_clean, ead_target_train_clean)
# Adding a constant column for the intercept for statsmodels
X = sm.add_constant(ead_inputs_train_clean)
y = ead_target_train_clean
# Fit the model using statsmodels
model = sm.OLS(y, X).fit()
# Extract the coefficients and p-values using statsmodels
coefficients = model.params
p_values = model.pvalues
# Create the summary table
summary_table = pd.DataFrame({
'Feature name': ['Intercept'] + ead_inputs_train_clean.columns.tolist(),
'Coefficients': coefficients,
'p_values': p_values
})
# Ensure the index is numeric before incrementing
if not pd.api.types.is_numeric_dtype(summary_table.index):
summary_table.index = pd.RangeIndex(start=1, stop=len(summary_table) + 1)
summary_table = summary_table.sort_index()
print(summary_table)
Feature name Coefficients p_values 1 Intercept -1.592158e-01 6.561218e-34 2 grade:A 3.519520e-01 2.502072e-269 3 grade:B 3.103346e-01 2.258541e-279 4 grade:C 2.636160e-01 9.577473e-269 5 grade:D 1.992982e-01 7.087881e-212 6 grade:E 1.383309e-01 4.466979e-131 7 grade:F 5.703972e-02 8.897831e-27 8 home_ownership:MORTGAGE -5.288195e-05 9.686354e-01 9 home_ownership:NONE -2.032282e-11 1.410731e-01 10 home_ownership:OWN 2.395827e-03 2.221073e-01 11 verification_status:Not Verified 9.159491e-03 7.898716e-08 12 verification_status:Source Verified -8.449963e-04 5.388667e-01 13 purpose:car 1.327407e-02 5.924564e-02 14 purpose:debt_consolidation 5.406436e-03 2.790360e-04 15 purpose:educational 4.166736e-12 1.409925e-01 16 purpose:home_improvement 2.284190e-02 2.593568e-16 17 purpose:house 2.229852e-02 6.559796e-03 18 purpose:moving 4.371317e-02 6.177058e-11 19 purpose:other 2.230007e-02 1.835860e-15 20 purpose:renewable_energy 5.480711e-02 7.887308e-03 21 purpose:small_business 2.763653e-02 1.609348e-07 22 purpose:vacation 3.594839e-02 1.592077e-06 23 purpose:wedding 2.816165e-01 1.399083e-01 24 initial_list_status:w 1.713212e-02 1.814723e-38 25 term_int 4.566211e-03 9.693554e-258 26 emp_length_int -1.600893e-03 6.050814e-25 27 funded_amnt 1.093319e-06 2.047493e-02 28 int_rate 2.651342e-02 0.000000e+00 29 installment -1.759477e-05 2.337459e-01 30 annual_inc 3.643881e-08 5.471899e-05 31 dti 1.345259e-04 1.587452e-02 32 delinq_2yrs -2.281786e-03 3.019012e-04 33 inq_last_6mths 3.007232e-03 3.159410e-06 34 mths_since_last_delinq -2.118475e-04 6.814813e-16 35 mths_since_last_record 1.620415e-05 5.188936e-01 36 open_acc -1.795607e-03 4.322330e-32 37 pub_rec -6.417389e-03 3.293860e-09 38 total_acc 4.211393e-04 4.348086e-09 39 acc_now_delinq -3.213338e-04 9.621434e-01 40 total_rev_hi_lim 3.173844e-07 1.077913e-35
In [98]:
ead_inputs_test =ead_inputs_test[features_all]
In [99]:
ead_inputs_test = ead_inputs_test.drop(features_reference_cat, axis = 1)
In [100]:
# Drop rows with NaN values from the test set
ead_inputs_test_clean = ead_inputs_test.dropna()
# Make predictions on the cleaned test data
y_hat_test_ead = reg_ead.predict(ead_inputs_test_clean)
In [101]:
ead_target_test_temp = ead_target_test
In [102]:
ead_target_test_temp = ead_target_test_temp.reset_index(drop = True)
In [103]:
pd.concat([ead_target_test_temp, pd.DataFrame(y_hat_test_ead)], axis = 1).corr()
Out[103]:
| CCF | 0 | |
|---|---|---|
| CCF | 1.00000 | 0.00801 |
| 0 | 0.00801 | 1.00000 |
In [104]:
import seaborn as sns
import matplotlib.pyplot as plt
# Drop NaN values in both the features and the target data simultaneously to ensure alignment
data_combined_test = pd.concat([ead_inputs_test, ead_target_test], axis=1)
data_combined_test_cleaned = data_combined_test.dropna()
# Split the cleaned data back into features and target
ead_inputs_test_cleaned = data_combined_test_cleaned.drop(columns=ead_target_test.name)
ead_target_test_cleaned = data_combined_test_cleaned[ead_target_test.name]
# Now make predictions using the cleaned data
y_hat_test_ead = reg_ead.predict(ead_inputs_test_cleaned)
# Check if the lengths of the target and predicted values are now the same
print(f"Length of ead_target_test_cleaned: {len(ead_target_test_cleaned)}")
print(f"Length of y_hat_test_ead: {len(y_hat_test_ead)}")
# Plot the distribution of residuals (errors)
residuals = ead_target_test_cleaned - y_hat_test_ead
# Create the plot
sns.histplot(residuals, kde=True, color="blue", bins=30)
# Add labels to the plot
plt.title('Distribution of Residuals for EAD Test Data')
plt.xlabel('Residuals (Actual - Predicted)')
plt.ylabel('Frequency')
# Show the plot
plt.show()
Length of ead_target_test_cleaned: 25828 Length of y_hat_test_ead: 25828
--------------------------------------------------------------------------- OptionError Traceback (most recent call last) ~\AppData\Local\Temp/ipykernel_2444/1662024484.py in <module> 21 22 # Create the plot ---> 23 sns.histplot(residuals, kde=True, color="blue", bins=30) 24 25 # Add labels to the plot ~\anaconda3\lib\site-packages\seaborn\distributions.py in histplot(data, x, y, hue, weights, stat, bins, binwidth, binrange, discrete, cumulative, common_bins, common_norm, multiple, element, fill, shrink, kde, kde_kws, line_kws, thresh, pthresh, pmax, cbar, cbar_ax, cbar_kws, palette, hue_order, hue_norm, color, log_scale, legend, ax, **kwargs) 1460 if p.univariate: 1461 -> 1462 p.plot_univariate_histogram( 1463 multiple=multiple, 1464 element=element, ~\anaconda3\lib\site-packages\seaborn\distributions.py in plot_univariate_histogram(self, multiple, element, fill, common_norm, common_bins, shrink, kde, kde_kws, color, legend, line_kws, estimate_kws, **plot_kws) 416 kde_kws["cumulative"] = estimate_kws["cumulative"] 417 log_scale = self._log_scaled(self.data_variable) --> 418 densities = self._compute_univariate_density( 419 self.data_variable, 420 common_norm, ~\anaconda3\lib\site-packages\seaborn\distributions.py in _compute_univariate_density(self, data_variable, common_norm, common_grid, estimate_kws, log_scale, warn_singular) 301 densities = {} 302 --> 303 for sub_vars, sub_data in self.iter_data("hue", from_comp_data=True): 304 305 # Extract the data points from this sub set and remove nulls ~\anaconda3\lib\site-packages\seaborn\_core.py in iter_data(self, grouping_vars, reverse, from_comp_data) 981 982 if from_comp_data: --> 983 data = self.comp_data 984 else: 985 data = self.plot_data ~\anaconda3\lib\site-packages\seaborn\_core.py in comp_data(self) 1052 # Use the converter assigned to the axis to get a float representation 1053 # of the data, passing np.nan or pd.NA through (pd.NA becomes np.nan) -> 1054 with pd.option_context('mode.use_inf_as_null', True): 1055 orig = self.plot_data[var].dropna() 1056 comp_col = pd.Series(index=orig.index, dtype=float, name=var) ~\anaconda3\lib\site-packages\pandas\_config\config.py in __enter__(self) 478 if not isinstance(cursor, dict): 479 raise OptionError(msg.format(option=".".join(path[:-1]))) --> 480 481 cursor[path[-1]] = defval # initialize 482 ~\anaconda3\lib\site-packages\pandas\_config\config.py in <listcomp>(.0) 478 if not isinstance(cursor, dict): 479 raise OptionError(msg.format(option=".".join(path[:-1]))) --> 480 481 cursor[path[-1]] = defval # initialize 482 ~\anaconda3\lib\site-packages\pandas\_config\config.py in _get_option(pat, silent) 144 def _describe_option(pat: str = "", _print_desc: bool = True): 145 --> 146 keys = _select_options(pat) 147 if len(keys) == 0: 148 raise OptionError("No such keys(s)") ~\anaconda3\lib\site-packages\pandas\_config\config.py in _get_single_key(pat, silent) 130 o.validator(v) 131 --> 132 # walk the nested dict 133 root, k = _get_root(key) 134 root[k] = v OptionError: "No such keys(s): 'mode.use_inf_as_null'"
In [105]:
pd.DataFrame(y_hat_test_ead).describe()
Out[105]:
| 0 | |
|---|---|
| count | 25828.000000 |
| mean | 0.706557 |
| std | 0.096581 |
| min | 0.360016 |
| 25% | 0.629772 |
| 50% | 0.691390 |
| 75% | 0.779098 |
| max | 1.126921 |
In [106]:
y_hat_test_ead = np.where(y_hat_test_ead < 0, 0, y_hat_test_ead)
y_hat_test_ead = np.where(y_hat_test_ead >1 ,1 , y_hat_test_ead)
In [107]:
pd.DataFrame(y_hat_test_ead).describe()
Out[107]:
| 0 | |
|---|---|
| count | 25828.000000 |
| mean | 0.706509 |
| std | 0.096426 |
| min | 0.360016 |
| 25% | 0.629772 |
| 50% | 0.691390 |
| 75% | 0.779098 |
| max | 1.000000 |
In [108]:
loan_data_preprocessed = loan_data
In [109]:
loan_data_preprocessed.head()
Out[109]:
| id | member_id | loan_amnt | funded_amnt | funded_amnt_inv | term | int_rate | installment | grade | sub_grade | ... | addr_state:UT | addr_state:VA | addr_state:VT | addr_state:WA | addr_state:WI | addr_state:WV | addr_state:WY | initial_list_status:f | initial_list_status:w | good_bad | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 68407277 | NaN | 3600.0 | 3600.0 | 3600.0 | 36 months | 13.99 | 123.03 | C | C4 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 1 | 68355089 | NaN | 24700.0 | 24700.0 | 24700.0 | 36 months | 11.99 | 820.28 | C | C1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 2 | 68341763 | NaN | 20000.0 | 20000.0 | 20000.0 | 60 months | 10.78 | 432.66 | B | B4 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 3 | 66310712 | NaN | 35000.0 | 35000.0 | 35000.0 | 60 months | 14.85 | 829.90 | C | C5 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 4 | 68476807 | NaN | 10400.0 | 10400.0 | 10400.0 | 60 months | 22.45 | 289.91 | F | F1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
5 rows × 279 columns
In [111]:
# This will save the Dask DataFrame as a single CSV file that you can open in Excel
loan_data_preprocessed.to_csv(r"C:\Users\computer\Desktop\loan data preprocessed.csv")
In [ ]:
loan_data_preprocessed['mths_since_last_delinq'].fillna(0,inplace = True)
In [ ]:
loan_data_preprocessed['mths_since_last_record'].fillna(0,inplace = True)
In [ ]:
loan_data_preprocessed_lgd_ead = loan_data_preprocessed[features_all]
In [ ]:
loan_data_preprocessed_lgd_ead = loan_data_preprocessed_lgd_ead.drop(features_reference_cat, axis = 1)
In [ ]:
# Step 1: Drop rows with missing values from the relevant columns in the original DataFrame
loan_data_preprocessed_cleaned = loan_data_preprocessed.dropna(subset=loan_data_preprocessed_lgd_ead.columns)
loan_data_preprocessed_lgd_ead_cleaned = loan_data_preprocessed_lgd_ead.dropna()
# Step 2: Make predictions using the cleaned data
predictions_st_1 = reg_lgd_st_1.model.predict(loan_data_preprocessed_lgd_ead_cleaned)
# Step 3: Initialize 'recovery_rate_st_1' column with NaN values in the original DataFrame
loan_data_preprocessed['recovery_rate_st_1'] = np.nan
# Step 4: Assign predictions to the corresponding rows in the original DataFrame
loan_data_preprocessed.loc[loan_data_preprocessed_cleaned.index, 'recovery_rate_st_1'] = predictions_st_1
In [ ]:
# Step 1: Drop rows with missing values from the feature matrix
loan_data_preprocessed_lgd_ead_cleaned = loan_data_preprocessed_lgd_ead.dropna()
# Step 2: Make predictions using the cleaned data
predictions_st_2 = reg_lgd_st_2.predict(loan_data_preprocessed_lgd_ead_cleaned)
# Step 3: Initialize the 'recovery_rate_st_2' column with NaN values
loan_data_preprocessed['recovery_rate_st_2'] = np.nan
# Step 4: Assign predictions back to the rows that were cleaned (i.e., dropped NaN)
loan_data_preprocessed.loc[loan_data_preprocessed_lgd_ead_cleaned.index, 'recovery_rate_st_2'] = predictions_st_2
In [ ]:
loan_data_preprocessed['recovery_rate'] = loan_data_preprocessed.loc[loan_data_preprocessed_cleaned.index, 'recovery_rate_st_1'] * loan_data_preprocessed.loc[loan_data_preprocessed_lgd_ead_cleaned.index, 'recovery_rate_st_2']
In [ ]:
loan_data_preprocessed['recovery_rate'] = np.where(loan_data_preprocessed['recovery_rate'] < 0, 0,loan_data_preprocessed['recovery_rate'] )
loan_data_preprocessed['recovery_rate'] = np.where(loan_data_preprocessed['recovery_rate'] > 1, 1,loan_data_preprocessed['recovery_rate'] )
In [ ]:
loan_data_preprocessed['LGD'] = 1 - loan_data_preprocessed['recovery_rate']
In [ ]:
loan_data_preprocessed['recovery_rate'].describe()
In [ ]:
import numpy as np
# Step 1: Drop rows with missing values from the feature matrix (for CCF prediction)
loan_data_preprocessed_lgd_ead_cleaned = loan_data_preprocessed_lgd_ead.dropna()
# Step 2: Make predictions using the cleaned data
predictions_ccf = reg_ead.predict(loan_data_preprocessed_lgd_ead_cleaned)
# Step 3: Initialize the 'CCF' column with NaN values in the original DataFrame
loan_data_preprocessed['CCF'] = np.nan
# Step 4: Assign predictions back to the rows that were cleaned (i.e., dropped NaN)
loan_data_preprocessed.loc[loan_data_preprocessed_lgd_ead_cleaned.index, 'CCF'] = predictions_ccf
# Optionally, check the updated DataFrame
print(loan_data_preprocessed[['CCF']].head())
In [ ]:
loan_data_preprocessed['CCF'] = np.where(loan_data_preprocessed['CCF'] < 0, 0,loan_data_preprocessed['CCF'] )
loan_data_preprocessed['CCF'] = np.where(loan_data_preprocessed['CCF'] > 1, 1,loan_data_preprocessed['CCF'] )
In [ ]:
loan_data_preprocessed['EAD'] = loan_data_preprocessed['CCF'] * loan_data_preprocessed_lgd_ead['funded_amnt']
In [ ]:
loan_data_preprocessed['EAD'].describe()
In [ ]:
loan_data_preprocessed.head()
In [1]:
import pandas as pd
import numpy as np
loan_data_inputs_train = pd.read_csv(r"C:\Users\computer\Desktop\loan data inputs train.csv")
loan_data_inputs_test = pd.read_csv(r"C:\Users\computer\Desktop\loan data inputs test.csv")
C:\Users\computer\AppData\Local\Temp\ipykernel_13756\2970929099.py:3: DtypeWarning: Columns (1,20) have mixed types. Specify dtype option on import or set low_memory=False. loan_data_inputs_train = pd.read_csv(r"C:\Users\computer\Desktop\loan data inputs train.csv") C:\Users\computer\AppData\Local\Temp\ipykernel_13756\2970929099.py:4: DtypeWarning: Columns (1,20) have mixed types. Specify dtype option on import or set low_memory=False. loan_data_inputs_test = pd.read_csv(r"C:\Users\computer\Desktop\loan data inputs test.csv")
In [2]:
loan_data_inputs_pd = pd.concat([loan_data_inputs_train ,loan_data_inputs_test], axis = 0)
In [3]:
loan_data_inputs_pd.shape
Out[3]:
(1048575, 220)
In [4]:
loan_data_inputs_pd.head()
Out[4]:
| Unnamed: 0 | id | member_id | loan_amnt | funded_amnt | funded_amnt_inv | term | int_rate | installment | grade | ... | annual.inc:440000.0-550000.0 | annual_inc:550000.0-660000.0 | mths_since_last_delinq:Missing | mths_since_last_deling:<17.9 | mths_since_last_deling:17.9-75.18 | mths_since_last_deling:75.18-100.24 | mths_since_last_deling:100.24-179 | mths_since_last_record_factor | mths_since_last_record_factor:<27.28 | mths_since_last_record_factor:27.28-124.0 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 408561 | 38427753 | NaN | 28000.0 | 28000.0 | 28000.0 | 36 months | 7.49 | 870.85 | A | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | NaN | 1 | 0 |
| 1 | 70143 | 63988897 | NaN | 22400.0 | 22400.0 | 22400.0 | 60 months | 17.86 | 567.11 | D | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | (79.36, 81.84] | 0 | 0 |
| 2 | 708782 | 77991627 | NaN | 5400.0 | 5400.0 | 5400.0 | 36 months | 11.47 | 178.00 | B | ... | 0 | 0 | 0 | 0 | 1 | 0 | 0 | (39.68, 42.16] | 0 | 1 |
| 3 | 572694 | 114833150 | NaN | 8975.0 | 8975.0 | 8975.0 | 36 months | 13.59 | 304.97 | C | ... | 0 | 0 | 0 | 0 | 1 | 0 | 0 | NaN | 1 | 0 |
| 4 | 774181 | 139300854 | NaN | 3600.0 | 3600.0 | 3600.0 | 36 months | 14.47 | 123.87 | C | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | NaN | 1 | 0 |
5 rows × 220 columns
In [ ]:
# This will save the Dask DataFrame as a single CSV file that you can open in Excel
loan_data_inputs_pd.to_csv(r'C:\Users\computer\Desktop\loan data inputs pd.csv')
In [5]:
features_all_pd = [ 'grade:A',
'grade:B',
'grade:C',
'grade:D',
'grade:E',
'grade:F',
'grade:G',
'home_ownership:MORTGAGE',
'home_ownership:OWN',
'home_ownership:RENT',
'addr_state:AL_AR',
'addr_state:OK_MS_SD_LA_NM_NV_HI',
'addr_state:NY_FL_IN_NE_NC_KY_MO_MD_OH_CA_PA_NJ_AZ_AK_MI_TN_MN_TX_VA_DE',
'addr_state:MA_UT_QI_GA_WY_RI_IL_ND_MT_KS_CT_CO_WV_WA_SC_DC_NH_OR_VT_ME',
'verification_status:Verified',
'verification_status:Source_Verified',
'verification_status:Not_Verified',
'purpose:small_business_moving_debt_consolidation',
'purpose:medical_house_other_renewable_energy_major_purchase',
'purpose:home_improvement_vacation_car_credit_card',
'term:36',
'term:60',
'initial_list_status:w',
'initial_list_status:f',
'emp_length:0',
'emp_length:1',
'emp_length:3',
'emp_length:4',
'emp_length:5-6',
'emp_length:7',
'emp_length:8',
'emp_length:9',
'emp_length:10',
'mth_earliest_cr_line_date_factor:111-134',
'mth_earliest_cr_line_date_factor:135-156',
'mth_earliest_cr_line_date_factor:157-414',
'mth_earliest_cr_line_date_factor:415-515',
'mth_earliest_cr_line_date_factor:516-671',
'int_rate:<16.609',
'int_rate:19.177-30.99',
'installment_factor:13-321',
'installment_factor:322-1310',
'installment_factor:1311-1719']
In [6]:
ref_categories_pd = [
'grade:G',
'home_ownership:RENT',
'addr_state:AL_AR',
'verification_status:Verified',
'purpose:small_business_moving_debt_consolidation',
'term:60',
'emp_length:0',
'initial_list_status:f',
'mth_earliest_cr_line_date_factor:111-134',
'int_rate:<16.609',
]
In [7]:
loan_data_inputs_pd_temp = loan_data_inputs_pd[features_all_pd]
In [8]:
loan_data_inputs_pd_temp = loan_data_inputs_pd_temp.drop(ref_categories_pd, axis =1)
In [9]:
loan_data_inputs_pd_temp.shape
Out[9]:
(1048575, 33)
In [10]:
import pickle
In [11]:
# Load the model from the .sav file
with open('pd_model.sav', 'rb') as f:
loaded_reg_model = pickle.load(f)
# Use the loaded model to predict probabilities
predicted_proba = loaded_reg_model.predict_proba(loan_data_inputs_pd_temp)[:, 0]
print(predicted_proba)
--------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) Cell In[11], line 3 1 # Load the model from the .sav file 2 with open('pd_model.sav', 'rb') as f: ----> 3 loaded_reg_model = pickle.load(f) 5 # Use the loaded model to predict probabilities 6 predicted_proba = loaded_reg_model.predict_proba(loan_data_inputs_pd_temp)[:, 0] ModuleNotFoundError: No module named 'sklearn'
In [ ]:
loan_data_inputs_pd['PD'] = predicted_proba
In [ ]:
loan_data_inputs_pd['PD'].head()
In [ ]:
# Import the required libraries
import dask.dataframe as dd
# Assuming the data can be loaded from CSV/Parquet, re-read it:
loan_data_preprocessed = dd.read_parquet(r"C:\Users\computer\Desktop\loan data preprocessed.parquet")
loan_data_inputs_pd = dd.read_parquet(r'C:\Users\computer\Desktop\loan data inputs pd.parquet')
# Proceed with further operations
loan_data_preprocessed = loan_data_preprocessed.persist()
loan_data_inputs_pd = loan_data_inputs_pd.persist()
# Concatenate the dataframes along axis=1
result = dd.concat([loan_data_preprocessed, loan_data_inputs_pd], axis=1)
result = result.persist()
# Save the result as a Parquet file
result.to_parquet(r'C:\Users\computer\Desktop\loan_data_combined_result.parquet')
# Close Dask client and cluster
client.close()
In [13]:
loan_data_preprocessed_new['EL'] = loan_data_preprocessed_new['PD'] * loan_data_preprocessed_new['LGD'] * loan_data_preprocessed_new['EAD']
--------------------------------------------------------------------------- NameError Traceback (most recent call last) Cell In[13], line 1 ----> 1 loan_data_preprocessed_new['EL'] = loan_data_preprocessed_new['PD'] * loan_data_preprocessed_new['LGD'] * loan_data_preprocessed_new['EAD'] NameError: name 'loan_data_preprocessed_new' is not defined
In [ ]:
loan_data_preprocessed_new['EL'].describe()
In [ ]: